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Number of items at this level: 347.

2023

Bengs, Viktor

; Hüllermeier, Eyke

and Waegeman, Willem

(2023): On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. 40th International Conference on Machine Learning (ICML 2023), Hawaii, USA, 23-29 July, 2023. In: Krause, Andreas; Brunskill, Emma; Cho, Kyunghyun; Engelhardt, Barbara; Sabato, Sivan and Scarlett, Jonathan (eds.) : Proceedings of the 40th International Conference on Machine Learning. Vol. 202. PMLR. pp. 2078-2091

Brandt, Jasmin; Schede, Elias; Haddenhorst, Björn

; Bengs, Viktor

; Hüllermeier, Eyke

and Tierney, Kevin (7. February 2023): AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI Conference on Artificial Intelligence, Washington, DC, USA, February, 2023. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 37. pp. 12355-12363

Dellnitz, Michael; Hüllermeier, Eyke

; Lücke, Marvin

; Ober-Blöbaum, Sina; Offen, Christian

; Peitz, Sebastian

and Pfannschmidt, Karlson

(2023): Efficient Time-Stepping for Numerical Integration Using Reinforcement Learning. In: SIAM Journal on Scientific Computing, Vol. 45, No. 2: A579-A595

Gupta, Pritha

; Drees, Jan Peter

and Hüllermeier, Eyke

(2023): Automated Side-Channel Attacks using Black-Box Neural Architecture Search. 18th International Conference on Availability, Reliability and Security, Benevento, Italy, 29 August - 1 September 2023. In: Proceedings of the 18th International Conference on Availability, Reliability and Security. New York, NY, USA: Association for Computing Machinery. pp. 1-11

Haas, Stefan

and Hüllermeier, Eyke

(18. September 2023): Rectifying Bias in Ordinal Observational Data Using Unimodal Label Smoothing. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Torino, Italy, 18-22 September, 2023. In: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. Vol. 14174. pp. 3-18

Hanselle, Jonas; Fürnkranz, Johannes and Hüllermeier, Eyke (2023): Probabilistic Scoring Lists for Interpretable Machine Learning. 26th International Conference on Discovery Science (DS 2023), Porto, Portugal, October 9-11, 2023. In: Discovery Science. Vol. 14276. Cham: Springer. pp. 189-203

Javanmardi, Alireza and Hüllermeier, Eyke

(24. July 2023): Conformal Prediction Intervals for Remaining Useful Lifetime Estimation. In: International Journal of Prognostics and Health Management, Vol. 14, No. 2

Javanmardi, Alireza; Sale, Yusuf; Hofman, Paul and Hüllermeier, Eyke

(September 2023): Conformal Prediction with Partially Labeled Data. Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023), Limassol, Cyprus, September 13-15, 2023. In: Papadopoulos, Harris; Nguyen, Khuong An; Boström, Henrik and Carlsson, Lars (eds.) : Vol. 204. PMLR. pp. 251-266

Lienen, Julian

; Demir, Caglar and Hüllermeier, Eyke

(September 2023): Conformal Credal Self-Supervised Learning. Twelfth Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023), Limassol, Cyprus, September 13-15, 2023. In: Papadopoulos, Harris; Nguyen, Khuong An; Boström, Henrik and Carlsson, Lars (eds.) : Vol. 204. PMLR. pp. 214-233

Mohr, Felix

and Wever, Marcel

(April 2023): Naive automated machine learning. In: Machine Learning, Vol. 112, No. 4: pp. 1131-1170

Mortier, Thomas

; Bengs, Viktor

; Hüllermeier, Eyke

; Luca, Stijn and Waegeman, Willem

(April 2023): On the Calibration of Probabilistic Classifier Sets. 26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Valencia, Spain, 25-27 April, 2023. In: Ruiz, Francisco; Dy, Jennifer and van de Meent, Jan-Willem (eds.) : Proceedings of The 26th International Conference on Artificial Intelligence and Statistics. Vol. 206. PMLR. pp. 8857-8870

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

and Hüllermeier, Eyke

(18. September 2023): iSAGE: An Incremental Version of SAGE for Online Explanation on Data Streams. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, Torino, Italy, 18-22 September, 2023. In: Machine Learning and Knowledge Discovery in Databases: Research Track. Vol. 14171. Cham: Springer. pp. 428-445

Nguyen, Duc Anh; Levie, Ron; Lienen, Julian

; Hüllermeier, Eyke

and Kutyniok, Gitta (May 2023): Memorization-Dilation: Modeling Neural Collapse Under Noise. The Eleventh International Conference on Learning Representations, Kigali, Rwanda, 1-5 May, 2023. In:

Sale, Yusuf; Caprio, Michele and Hüllermeier, Eyke

(August 2023): Is the volume of a credal set a good measure for epistemic uncertainty? Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA, 1-3 August, 2023. In: Evans, Robin J. and Shpitser, Ilya (eds.) : Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Vol. 216. PMLR. pp. 1795-1804

Schede, Elias; Brandt, Jasmin; Tornede, Alexander

; Wever, Marcel

; Bengs, Viktor

; Hüllermeier, Eyke

and Tierney, Kevin (19. August 2023): A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI 2023), Macao, S.A.R, 19-25 August 2023. In: Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence. pp. 6964-6968

Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke

and Wehrheim, Heike (February 2023): Property-Driven Black-Box Testing of Numeric Functions. Software Engineering 2023, Paderborn, Germany, 20-24 February 2023. In: Engels, Gregor; Hebig, Regina and Tichy, Matthias (eds.) : Bonn: Gesellschaft für Informatik e.V.. pp. 111-112 [PDF, 213kB]

Tornede, Alexander

; Gehring, Lukas; Tornede, Tanja

; Wever, Marcel

and Hüllermeier, Eyke

(2023): Algorithm selection on a meta level. In: Machine Learning, Vol. 112, No. 4: pp. 1253-1286

Tornede, Tanja

; Tornede, Alexander

; Fehring, Lukas

; Gehring, Lukas; Graf, Helena

; Hanselle, Jonas

; Mohr, Felix

and Wever, Marcel

(20. April 2023): PyExperimenter: Easily distribute experiments and track results. In: Journal of Open Source Software, Vol. 8, No. 84: p. 5149

Tornede, Tanja

; Tornede, Alexander

; Hanselle, Jonas; Mohr, Felix

; Wever, Marcel

and Hüllermeier, Eyke

(2023): Towards Green Automated Machine Learning: Status Quo and Future Directions. In: Journal of Artificial Intelligence Research, Vol. 77: pp. 427-457

Wever, Marcel

; Özdogan, Miran and Hüllermeier, Eyke

(2023): Cooperative Co-Evolution for Ensembles of Nested Dichotomies for Multi-Class Classification. Genetic and Evolutionary Computation Conference (GECCO '23), Lisbon, Portugal, July 15-19, 2023. In: Proceedings of the Genetic and Evolutionary Computation Conference. New York, NY, USA: Association for Computing Machinery. 597–605

Wickert, Anna-Katharina

; Damke, Clemens

; Baumgärtner, Lars

; Hüllermeier, Eyke

and Mezini, Mira

(15. May 2023): UnGoML: Automated Classification of unsafe Usages in Go. 20th International Conference on Mining Software Repositories (MSR), Melbourne, Australia, 15-16 May, 2023. In: 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR). IEEE. pp. 309-321

Wimmer, Lisa; Sale, Yusuf; Hofman, Paul; Bischl, Bernd

and Hüllermeier, Eyke

(2023): Quantifying aleatoric and epistemic uncertainty in machine learning: Are conditional entropy and mutual information appropriate measures? Thirty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI 2023), Pittsburgh, PA, USA, 1-3 August, 2023. In: Evans, Robin J. and Shpitser, Ilya (eds.) : Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Vol. 216. PMLR. pp. 2282-2292

2022

Bengs, Viktor

and Hüllermeier, Eyke

(16. November 2022): Multi-armed bandits with censored consumption of resources. In: Machine Learning, Vol. 112, No. 2: pp. 217-240

Bengs, Viktor

; Hüllermeier, Eyke

and Waegeman, Willem

(28. November 2022): Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. Advances in Neural Information Processing Systems, New Orleans, USA, 28 November - 9 December 2022. In: Oh, Alice H.; Agarwal, Alekh; Belgrave, Danielle and Cho, Kyunghyun (eds.) :

Bengs, Viktor; Saha, Aadirupa and Hüllermeier, Eyke

(July 2022): Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. 39th International Conference on Machine Learning, Baltimore, MD, USA, July 17-23 2022. In: Chaudhuri, Kamalika; Jegelka, Stefanie; Song, Le; Szepesvari, Csaba; Niu, Gang and Sabato, Sivan (eds.) : Proceedings of the 39th International Conference on Machine Learning. Vol. 162. PMLR. pp. 1764-1786

Brandt, Jasmin; Bengs, Viktor

; Haddenhorst, Björn

and Hüllermeier, Eyke

(28. November 2022): Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. Advances in Neural Information Processing Systems, New Orleans, USA, 28 November - 9 December 2022. In: Oh, Alice H.; Agarwal, Alekh; Belgrave, Danielle and Cho, Kyunghyun (eds.) :

Campagner, Andrea

; Lienen, Julian

; Hüllermeier, Eyke

and Ciucci, Davide

(2022): Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. International Joint Conference on Rough Sets, Suzhou, China, 11-14 November 2022. In: Rough Sets. Vol. 13633. Vol. 13633. Cham: Springer. pp. 57-70

Gupta, Pritha

; Ramaswamy, Arunselvan

; Drees, Jan Peter

; Hüllermeier, Eyke

; Priesterjahn, Claudia

and Jager, Tibor

(February 2022): Automated Information Leakage Detection: A New Method Combining Machine Learning and Hypothesis Testing with an Application to Side-channel Detection in Cryptographic Protocols. 14th International Conference on Agents and Artificial Intelligence (ICAART 2022), Virtual, February 3-5. In: Proceedings of the 14th International Conference on Agents and Artificial Intelligence. Vol. 2. pp. 152-163

Haas, Stefan

and Hüllermeier, Eyke

(September 2022): A Prescriptive Machine Learning Approach for Assessing Goodwill in the Automotive Domain. Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022), Grenoble, France, 19-23 September, 2023. In: Machine Learning and Knowledge Discovery in Databases. Vol. 13718. Cham: Springer. pp. 170-184

Hüllermeier, Eyke

; Destercke, Sébastien and Shaker, Mohammad Hossein (August 2022): Quantification of Credal Uncertainty in Machine Learning: A Critical Analysis and Empirical Comparison. 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022. In: Cussens, James and Zhang, Kun (eds.) : Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence. Vol. 180. PMLR. pp. 548-557

Hüllermeier, Eyke

; Wever, Marcel; Loza Mencia, Eneldo; Fürnkranz, Johannes and Rapp, Michael

(February 2022): A flexible class of dependence-aware multi-label loss functions. In: Machine Learning, Vol. 111, No. 2: pp. 713-737 [PDF, 1MB]

Mortier, Thomas

; Hüllermeier, Eyke

; Dembczyński, Krzysztof and Waegeman, Willem

(August 2022): Set-valued prediction in hierarchical classification with constrained representation complexity. 38th Conference on Uncertainty in Artificial Intelligence, Eindhoven, Netherlands, 1-5 August 2022. In: Cussens, James and Zhang, Kun (eds.) : Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence. Vol. 180. PMLR. pp. 1392-1401

Muschalik, Maximilian

; Fumagalli, Fabian

; Hammer, Barbara

and Hüllermeier, Eyke

(July 2022): Agnostic Explanation of Model Change based on Feature Importance. In: KI - Künstliche Intelligenz, Vol. 36, No. 3-4: pp. 211-224

Nguyen, Vu-Linh; Shaker, Mohammad Hossein and Hüllermeier, Eyke

(January 2022): How to measure uncertainty in uncertainty sampling for active learning. In: Machine Learning, Vol. 111, No. 1: pp. 89-122

Pfannschmidt, Karlson

; Gupta, Pritha

; Haddenhorst, Björn and Hüllermeier, Eyke

(January 2022): Learning context-dependent choice functions. In: International Journal of Approximate Reasoning, Vol. 140: pp. 116-155

Rodemann, Julian

; Kreiss, Dominik; Hüllermeier, Eyke

and Augustin, Thomas (October 2022): Levelwise Data Disambiguation by Cautious Superset Classification. 15th International Conference on Scalable Uncertainty Management, Paris, France, 17-19 October 2022. In: Proceedings of the 15th International Conference on Scalable Uncertainty Management. Vol. 13562. Vol. 13562. Cham: Springer. pp. 263-276

Schede, Elias; Brandt, Jasmin; Tornede, Alexander

; Wever, Marcel

; Bengs, Viktor

; Hüllermeier, Eyke

and Tierney, Kevin (October 2022): A Survey of Methods for Automated Algorithm Configuration. In: Journal of Artificial Intelligence Research, Vol. 75: pp. 425-487

Schneider, Stefanie

; Springstein, Matthias; Rahnama, Javad; Kohle, Hubertus

; Ewerth, Ralph

and Hüllermeier, Eyke

(March 2022): iART - Eine Suchmaschine zur Unterstützung von bildorientierten Forschungsprozessen. DHd 2022 Kulturen des digitalen Gedächtnisses. 8. Tagung des Verbands "Digital Humanities im deutschsprachigen Raum", Potsdam, 7. - 11. März 2022. In:

Sharma, Arnab; Melnikov, Vitalik; Hüllermeier, Eyke

and Wehrheim, Heike (May 2022): Property-Driven Testing of Black-Box Functions. 2022 IEEE/ACM 10th International Conference on Formal Methods in Software Engineering (FormaliSE), Pittsburgh, PA, USA, 22-23 May 2022. In: pp. 113-123

Tornede, Alexander

; Bengs, Viktor and Hüllermeier, Eyke

(June 2022): Machine Learning for Online Algorithm Selection under Censored Feedback. Thirty-Sixth AAAI Conference on Artificial Intelligence, Virtual, February 22–March 1, 2022. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 36. pp. 10370-10380

2021

Abbaszadeh, Sadegh

and Hüllermeier, Eyke

(December 2021): Machine Learning With the Sugeno Integral: The Case of Binary Classification. In: IEEE Transactions on Fuzzy Systems, No. 12: pp. 3723-3733

Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke (April 2021): Analogical Embedding for Analogy-Based Learning to Rank. Advances in Intelligent Data Analysis XIX. IDA 2021, Porto, Portugal, April 26-28, 2021. In: Advances in Intelligent Data Analysis XIX. Vol. 12695. Vol. 12695. Cham: Springer. pp. 76-88

Bengs, Viktor; Busa-Fekete, Róbert; Mesaoudi-Paul, Adil El and Hüllermeier, Eyke

(January 2021): Preference-based Online Learning with Dueling Bandits: A Survey. In: Journal of Machine Learning Research, Vol. 22, No. 7: pp. 1-108

Bresson, Roman; Cohen, Johanne; Hüllermeier, Eyke

; Labreuche, Christophe and Sebag, Michèle (3. November 2021): On the Identifiability of Hierarchical Decision Models. International Conference on Principles of Knowledge Representation and Reasoning, Virtual, November 3-12, 2021. In: Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning. pp. 151-162

Campagner, Andrea

; Ciucci, Davide

and Hüllermeier, Eyke

(June 2021): Rough set-based feature selection for weakly labeled data. In: International Journal of Approximate Reasoning: pp. 150-167

Damke, Clemens

and Hüllermeier, Eyke

(11. October 2021): Ranking Structured Objects with Graph Neural Networks. 24th International Conference on Discovery Science (DS 2021), Halifax, NS, Canada, Virtual, October 11–13, 2021. In: Soares, Carlos and Torgo, Luis (eds.) : Proceedings of the 24th International Conference on Discovery Science. Lecture Notes in Computer Science, Vol. 12986. Vol. 12986. Cham: Springer. pp. 166-180

Drees, Jan Peter; Gupta, Pritha

; Hüllermeier, Eyke

; Jager, Tibor; Konze, Alexander; Priesterjahn, Claudia; Ramaswamy, Arunselvan and Somorovsky, Juraj (November 2021): Automated Detection of Side Channels in Cryptographic Protocols: DROWN the ROBOTs! CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, 15. November 2021. In: Carlini, Nicholas (ed.) : Proceedings of the 14th ACM Workshop on Artificial Intelligence and Security. Vol. 22. New York: Association for Computing Machinery. pp. 169-180

Feldhans, Robert; Wilke, Adrian; Heindorf, Stefan; Shaker, Mohammad Hossein; Hammer, Barbara; Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke

(November 2021): Drift Detection in Text Data with Document Embeddings. Intelligent Data Engineering and Automated Learning – IDEAL 2021, Manchester, United Kingdom, November 25-27, 2021. In: Intelligent Data Engineering and Automated Learning – IDEAL 2021. Vol. 13113. Vol. 13113. Cham: Springer. pp. 107-118

Haddenhorst, Björn

; Bengs, Viktor; Brandt, Jasmin and Hüllermeier, Eyke

(27. July 2021): Testification of Condorcet Winners in dueling bandits. Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, Virtual, July 27-30, 2021. In: de Campos, Cassio and Maathuis, Marloes H. (eds.) : Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. Vol. 161. PMLR. pp. 1195-1205

Haddenhorst, Björn

; Bengs, Viktor and Hüllermeier, Eyke

(July 2021): On testing transitivity in online preference learning. In: Machine Learning, Vol. 110, No. 8: pp. 2063-2084

Haddenhorst, Björn

; Bengs, Viktor and Hüllermeier, Eyke

(7. December 2021): Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. Advances in Neural Information Processing Systems, Virtual, December 7 2021. In: Ranzato, M.; Beygelzimer, A.; Dauphin, Y.; Liang, P.S. and Vaughan, J. Wortman (eds.) : Vol. 34. Curran Associates, Inc.. pp. 25904-25916

Hanselle, Jonas; Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(May 2021): Algorithm Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance Data. In: Karlapalem, Kamal; Cheng, Hong; Ramakrishnan, Naren; Agrawal, R. K.; Krishna Reddy, P.; Srivastava, Jaideep and Chakraborty, Tanmoy (eds.) : (2021): Advances in Knowledge Discovery and Data Mining 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 12712. Cham: Springer. pp. 152-163

Hüllermeier, Eyke

; Mohr, Felix; Tornede, Alexander

and Wever, Marcel

(September 2021): Automated Machine Learning, Bounded Rationality, and Rational Metareasoning. ECMLPKDD Workshop on Automating Data Science (ADS2021), Virtual, 17 September 2021. In: arxiv.org.

Hüllermeier, Eyke

and Waegeman, Willem (March 2021): Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. In: Machine Learning, No. 3: pp. 457-506

Kolpaczki, Patrick; Bengs, Viktor and Hüllermeier, Eyke

(September 2021): Identifying Top-k Players in Cooperative Games via Shapley Bandits. LWDA’21: Lernen, Wissen, Daten, Analysen, Munich, Germany, 1. - 3. September, 2021. In: Proceedings of the LWDA 2021 Workshops: FGWM, KDML, FGWI-BIA, and FGIR. Vol. 2993. pp. 133-144

Lienen, Julian

and Hüllermeier, Eyke

(July 2021): Instance weighting through data imprecisiation. In: International Journal of Approximate Reasoning, Vol. 134: pp. 1-14

Lienen, Julian and Hüllermeier, Eyke

(February 2021): From Label Smoothing to Label Relaxation. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21), Virtual, February 2–9, 2021. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. pp. 8583-8591

Lienen, Julian

and Hüllermeier, Eyke

(December 2021): Credal Self-Supervised Learning. 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Virtual, December 7, 2021. In: Ranzato, M.; Beygelzimer, A.; Dauphin, Y.; Liang, P.S. and Vaughan, J. Wortman (eds.) : Advances in Neural Information Processing Systems. Vol. 34. Curran Associates, Inc.. pp. 14370-14382

Lienen, Julian; Hüllermeier, Eyke

; Ewerth, Ralph and Nommensen, Nils (June 2021): Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20-25 June 2021. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Piscataway, NJ: IEEE. pp. 14590-14599

Lienen, Julian

; Nommensen, Nils; Ewerth, Ralph and Hüllermeier, Eyke

(17. November 2021): Robust Regression for Monocular Depth Estimation. Asian Conference on Machine Learning, Virtual, November 17 - 19, 2021. In: Balasubramanian, Vineeth N. and Tsang, Ivor (eds.) : Proceedings of The 13th Asian Conference on Machine Learning. Vol. 157. PMLR. pp. 1001-1016

Mohr, Felix

; Bengs, Viktor and Hüllermeier, Eyke

(February 2021): Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI Conference on Artificial Intelligence, Virtual, February 2–9, 2021. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. pp. 12373-12381

Mohr, Felix and Wever, Marcel

(July 2021): Replacing the Ex-Def Baseline in AutoML by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual, 2021-07-23. In:

Mohr, Felix

; Wever, Marcel

; Tornede, Alexander

and Hüllermeier, Eyke

(February 2021): Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 9: pp. 3055-3066

Mortier, Thomas

; Wydmuch, Marek; Dembczyński, Krzysztof; Hüllermeier, Eyke

and Waegeman, Willem (May 2021): Efficient set-valued prediction in multi-class classification. In: Data Mining and Knowledge Discovery, Vol. 35, No. 4: pp. 1435-1469

Nguyen, Vu-Linh

and Hüllermeier, Eyke

(2. November 2021): Multilabel Classification with Partial Abstention: Bayes-Optimal Prediction under Label Independence. In: Journal of Artificial Intelligence Research, Vol. 72: pp. 613-665

Peeters, Sven; Melnikov, Vitalik and Hüllermeier, Eyke

(26. April 2021): Performance Prediction for Hardware-Software Configurations: A Case Study for Video Games. 19th International Symposium on Intelligent Data Analysis, Porto, Portugal, April 26–28, 2021. In: Advances in Intelligent Data Analysis XIX. Vol. 12695. pp. 222-234

Ramaswamy, Arunselvan and Hüllermeier, Eyke

(September 2021): Deep Q-Learning: Theoretical Insights from an Asymptotic Analysis. In: IEEE Transactions on Artificial Intelligence: p. 1

Rapp, Michael

; Mencía, Eneldo Loza; Fürnkranz, Johannes and Hüllermeier, Eyke

(September 2021): Gradient-Based Label Binning in Multi-label Classification. Machine Learning and Knowledge Discovery in Databases, Bilbao, Spain, September 13–17, 2021. In: Machine Learning and Knowledge Discovery in Databases. Research Track. Vol. 12977. Vol. 12977. Cham: Springer. pp. 462-477

Rapp, Michael

; Mencía, Eneldo Loza; Fürnkranz, Johannes; Nguyen, Vu-Linh and Hüllermeier, Eyke

(February 2021): Learning Gradient Boosted Multi-label Classification Rules. In: Hutter, Frank; Kersting, Kristian; Lijffijt, Jefrey and Valera, Isabel (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part III. Lecture Notes in Computer Science, Vol. 12459. Cham: Springer. pp. 124-140

Rohlfing, Katharina J.

; Cimiano, Philipp; Scharlau, Ingrid

; Matzner, Tobias; Buhl, Heike M.; Buschmeier, Hendrik

; Esposito, Elena

; Grimminger, Angela

; Hammer, Barbara; Hab-Umbach, Reinhold

; Horwath, Ilona

; Hüllermeier, Eyke

; Kern, Friederike; Kopp, Stefan

; Thommes, Kirsten

; Ngonga Ngomo, Axel-Cyrille; Schulte, Carsten; Wachsmuth, Henning; Wagner, Petra

and Wrede, Britta (September 2021): Explanation as a Social Practice: Toward a Conceptual Framework for the Social Design of AI Systems. In: IEEE Transactions on Cognitive and Developmental Systems, Vol. 13, No. 3: pp. 717-728 [PDF, 1MB]

Schneider, Stefanie

; Springstein, Matthias; Rahnama, Javad; Hüllermeier, Eyke

; Ewerth, Ralph

and Kohle, Hubertus

(2021): The Dissimilar in the Similar. An Attribute-guided Approach to the Subject-specific Classification of Art-historical Objects. INFORMATIK 2020 - Back to the Future, Virtual, 28 September - 2 October 2020. In: Reussner, Ralf H.; Koziolek, Anne and Heinrich, Robert (eds.) : INFORMATIK 2020. Bonn: Gesellschaft für Informatik.

Shaker, Ammar and Hüllermeier, Eyke

(June 2021): TSK-Streams: learning TSK fuzzy systems for regression on data streams. In: Data Mining and Knowledge Discovery, No. 5: pp. 1941-1971

Springstein, Matthias; Schneider, Stefanie

; Rahnama, Javad; Hüllermeier, Eyke

; Kohle, Hubertus

and Ewerth, Ralph

(October 2021): iART: A Search Engine for Art-Historical Images to Support Research in the Humanities. 29th ACM International Conference on Multimedia, Virtual, October 20 - 24, 2021. In: Proceedings of the 29th ACM International Conference on Multimedia. pp. 2801-2803

Tornede, Tanja

; Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(July 2021): Coevolution of remaining useful lifetime estimation pipelines for automated predictive maintenance. GECCO '21, Proceedings of the Genetic and Evolutionary Computation Conference Companion, Lille, France, 10 - 14 July, 2021. In: Chicano, Francisco (ed.) : Proceedings of the Genetic and Evolutionary Computation Conference. New York, NY: Association for Computing Machinery. pp. 368-376

Weber, Daniel

; Heid, Stefan; Bode, Henrik; Lange, Jarren H.; Hüllermeier, Eyke

and Wallscheid, Oliver

(February 2021): Safe Bayesian Optimization for Data-Driven Power Electronics Control Design in Microgrids: From Simulations to Real-World Experiments. In: IEEE Access, Vol. 9: pp. 35654-35669

Wever, Marcel

(2021): Automated Machine Learning for Multi-Label Classification. Dissertation, Universität Paderborn
[PDF, 8MB]

Wever, Marcel

; Tornede, Alexander

; Mohr, Felix

and Hüllermeier, Eyke

(September 2021): AutoML for Multi-Label Classification: Overview and Empirical Evaluation. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 43, No. 9: pp. 3037-3054 [PDF, 2MB]

2020

Bengs, Viktor and Hüllermeier, Eyke

(July 2020): Preselection Bandits. 37th International Conference on Machine Learning, Virtual, July 12-18 2020. In: III, Hal Daumé and Singh, Aarti (eds.) : Proceedings of the 37th International Conference on Machine Learning. Vol. 119. PMLR. pp. 778-787

Bode, Henrik; Heid, Stefan; Weber, Daniel; Hüllermeier, Eyke

and Wallscheid, Oliver

(May 2020): Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. arxiv.org

Bresson, Roman; Cohen, Johanne; Hüllermeier, Eyke

; Labreuche, Christophe and Sebag, Michèle (2020): Neural Representation and Learning of Hierarchical 2-additive Choquet Integrals. Twenty-Ninth International Joint Conference on Artificial Intelligence, Yokohama, Japan (Virtual), January 7-15, 2021. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. pp. 1984-1991

Brinker, Klaus and Hüllermeier, Eyke

(April 2020): A Reduction of Label Ranking to Multiclass Classification. In: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part III. Lecture Notes in Computer Science, Vol. 11908. Cham: Springer. pp. 204-219

Campagner, Andrea

; Ciucci, Davide

and Hüllermeier, Eyke

(June 2020): Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Vol. 1237. Vol. 1237. Cham: Springer. pp. 471-484

Damke, Clemens

; Melnikov, Vitalik and Hüllermeier, Eyke

(November 2020): A Novel Higher-order Weisfeiler-Lehman Graph Convolution. 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020. In: Jialin Pan, Sinno and Sugiyama, Masashi (eds.) : Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol. 129. pp. 49-64

Dari, Simone and Hüllermeier, Eyke

(November 2020): Reliable Driver Gaze Classification based on Conformal Prediction. 30. Workshop Computational Intelligence, Berlin, Germany, 26. – 27. November 2020. In: Schulte, H.; Hoffmann, F. and Mikut, R. (eds.) : Proceedings. 30. Workshop Computational Intelligence Berlin, 26. – 27. November 2020. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), Vol. 29. Karlsruhe: KIT Scientific Publishing. pp. 1-15

Dari, Simone; Kadrileev, Nikolay and Hüllermeier, Eyke

(July 2020): A Neural Network-Based Driver Gaze Classification System with Vehicle Signals. 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19-24 July 2020. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1-7

El Mesaoudi-Paul, Adil; Bengs, Viktor and Hüllermeier, Eyke

(February 2020): Online Preselection with Context Information under the Plackett-Luce Model. arxiv.org

El Mesaoudi-Paul, Adil; Weiß, Dimitri; Bengs, Viktor; Hüllermeier, Eyke

and Tierney, Kevin (May 2020): Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Kotsireas, Ilias S. and Pardalos, Panos M. (eds.) : Learning and Intelligent Optimization. 14th International Conference, LION 14, Athens, Greece, May 24–28, 2020, Revised Selected Papers. Lecture Notes in Computer Science, Vol. 12096. Cham: Springer. pp. 216-232

Haddenhorst, Björn; Hüllermeier, Eyke

and Kolb, Martin (April 2020): Generalized transitivity: A systematic comparison of concepts with an application to preferences in the Babington Smith model. In: International Journal of Approximate Reasoning, Vol. 119: pp. 373-407

Hanselle, Jonas; Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(September 2020): Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020. In: Schmid, Ute; Klügl, Franziska and Wolter, Diedrich (eds.) : KI 2020: Advances in Artificial Intelligence, Proceedings. Lecture Notes in Computer Science, Vol. 12325. Vol. 12325. Cham: Springer. pp. 59-72

Heid, Stefan; Ramaswamy, Arunselvan and Hüllermeier, Eyke

(November 2020): Constrained Multi-Agent Optimization with Unbounded Information Delay. 30. Workshop Computational Intelligence, Berlin, Germany, 26. – 27. November 2020. In: Schulte, H.; Hoffmann, F. and Mikut, R. (eds.) : Proceedings. 30. Workshop Computational Intelligence Berlin, 26. – 27. November 2020. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), Vol. 29. Karlsruhe: KIT Scientific Publishing. pp. 247-261

Heid, Stefan; Weber, Daniel; Bode, Henrik; Hüllermeier, Eyke

and Wallscheid, Oliver

(October 2020): OMG: A Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid Control. In: Journal of Open Source Software (JOSS), Vol. 5, No. 54, 2435

Heid, Stefan; Wever, Marcel Dominik

and Hüllermeier, Eyke

(August 2020): Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.

Hüllermeier, Eyke

(September 2020): Towards Analogy-Based Explanations in Machine Learning. Modeling Decisions for Artificial Intelligence, Sant Cugat, Spain, September 2–4, 2020. In: Modeling Decisions for Artificial Intelligence. Vol. 12256. Vol. 12256. Cham: Springer. pp. 205-217

Hüllermeier, Eyke

; Fürnkranz, Johannes and Loza Mencia, Eneldo (2020): Conformal Rule-Based Multi-label Classification. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, Bamberg, Germany, September 21–25, 2020. In: KI 2020: Advances in Artificial Intelligence. Vol. 12325. Vol. 12325. Cham: Springer. pp. 290-296

Hüllermeier, Eyke

; Fürnkranz, Johannes; Loza Mencia, Eneldo; Nguyen, Vu-Linh

and Rapp, Michael

(August 2020): Rule-Based Multi-label Classification: Challenges and Opportunities. 4th International Joint Conference on Rules and Reasoning, Virtual, 29 June - 1 July 2020. In: Rules and Reasoning. Vol. 12173. Vol. 12173. Cham: Springer. pp. 3-19

Nguyen, Vu-Linh

and Hüllermeier, Eyke

(February 2020): Reliable Multilabel Classification: Prediction with Partial Abstention. 34th AAAI Conference on Artificial Intelligence, New York, USA, 7-12 Feburary 2020. In: Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. Vol. 34. pp. 5264-5271

Nguyen, Vu-Linh

; Hüllermeier, Eyke

; Rapp, Michael

; Loza Mencia, Eneldo and Fürnkranz, Johannes (15. October 2020): On Aggregation in Ensembles of Multilabel Classifiers. 23rd International Conference on Discovery Science, Virtual, 19-21 October 2020. In: Discovery Science. Vol. 12323. Vol. 12323. Cham: Springer. pp. 533-547

Pfannschmidt, Karlson

and Hüllermeier, Eyke

(2020): Learning Choice Functions via Pareto-Embeddings. 43rd German Conference on AI, Bamberg, Germany, 21-25 September, 2020. In: KI 2020: Advances in Artificial Intelligence. Vol. 12325. Vol. 12325. Cham: Springer. pp. 327-333

Rahnama, Javad and Hüllermeier, Eyke

(5. June 2020): Learning Tversky Similarity. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Lisbon, Portugal, 15-19 June 2020. In: Information Processing and Management of Uncertainty in Knowledge-Based Systems. Vol. 1238. Vol. 1238. Cham: Springer. pp. 269-280

Richter, Cedric; Hüllermeier, Eyke

; Jakobs, Marie-Christine and Wehrheim, Heike (April 2020): Algorithm selection for software validation based on graph kernels. In: Automated Software Engineering, Vol. 27, No. 1-2: pp. 153-186

Rohlfing, Katharina J.

; Leonardi, Giuseppe

; Nomikou, Iris

; Raczaszek-Leonardi, Joanna

and Hüllermeier, Eyke

(June 2020): Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. In: IEEE Transactions on Cognitive and Developmental Systems, Vol. 12, No. 2: pp. 260-271

Shaker, Mohammad Hossein and Hüllermeier, Eyke

(April 2020): Aleatoric and Epistemic Uncertainty with Random Forests. International Symposium on Intelligent Data Analysis, Bodenseeforum, Lake Constance, Germany, April 27-29 2020. In: Advances in Intelligent Data Analysis XVIII. Vol. 12080. Vol. 12080. Cham: Springer. pp. 444-456

Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(December 2020): Towards Meta-Algorithm Selection. 4th Workshop on Meta-Learning at NeurIPS, Virtual, 11 December 2020. In: arxiv.org.

Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(October 2020): Extreme Algorithm Selection with Dyadic Feature Representation. In: Appice, Annalisa; Tsoumakas, Grigorios; Manolopoulos, Yannis and Matwin, Stan (eds.) : (2021): Discovery Science. 23rd International Conference, DS 2020, Thessaloniki, Greece, October 19–21, 2020, Proceedings. Lecture Notes in Computer Science, Vol. 12323. Cham: Springer. pp. 309-324

Tornede, Alexander

; Wever, Marcel

; Werner, Stefan; Mohr, Felix and Hüllermeier, Eyke

(November 2020): Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis. 12th Asian Conference on Machine Learning (ACML 2020), Bangkok, Thailand, 18-20 November 2020. In: Jialin Pan, Sinno and Sugiyama, Masashi (eds.) : Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020). Vol. 129. pp. 737-752

Tornede, Tanja; Tornede, Alexander

; Wever, Marcel

; Mohr, Felix

and Hüllermeier, Eyke

(September 2020): AutoML for Predictive Maintenance: One Tool to RUL Them All. Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020. In: Gama, Joao (ed.) : IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, Septem. Vol. 1325. Cham: Springer. pp. 106-118

Wever, Marcel

; Tornede, Alexander

; Mohr, Felix and Hüllermeier, Eyke

(April 2020): LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-label Classification. In: Berthold, Michael; Feelders, Ad and Krempl, Georg (eds.) : (2020): Advances in Intelligent Data Analysis XVIII. 18th International Symposium on Intelligent Data Analysis, IDA 2020, Konstanz, Germany, April 27–29, 2020, Proceedings. Lecture Notes in Computer Science, Vol. 12080. New York, NY: Association for Computing Machinery. pp. 561-573 [PDF, 425kB]

Wever, Marcel

; van Rooijen, Lorijn and Hamann, Heiko (June 2020): Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets. In: Evolutionary Computation, Vol. 28, No. 2: pp. 165-193

2019

Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke

(2019): Analogy-Based Preference Learning with Kernels. In: Benzmüller, Christoph and Stuckenschmidt, Heiner (eds.) : KI 2019: Advances in Artificial Intelligence. 42nd German Conference on AI, Kassel, Germany, September 23–26, 2019, Proceedings. Lecture Notes in Computer Science, Vol. 11793. Cham: Springer. pp. 34-47

Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke

(2019): Feature Selection for Analogy-Based Learning to Rank. In: Kralj Novak, Petra; Šmuc, Tomislav and Džeroski, Sašo (eds.) : Discovery Science. 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings. Lecture Notes in Computer Science, Vol. 11828. Cham: Springer. pp. 279-289

Bengs, Viktor; Eulert, Matthias and Holzmann, Hajo (2019): Asymptotic confidence sets for the jump curve in bivariate regression problems. In: Journal of Multivariate Analysis, Vol. 173: pp. 291-312

Bengs, Viktor and Holzmann, Hajo (2019): Adaptive confidence sets for kink estimation. In: Electronic Journal of Statistics, Vol. 13, No. 1: pp. 1523-1579 [PDF, 726kB]

Couso, Ines; Borgelt, Christian; Hüllermeier, Eyke

and Kruse, Rudolf (2019): Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning. In: IEEE Computational Intelligence Magazine, Vol. 14, No. 1: pp. 31-44

Henzgen, Sascha and Hüllermeier, Eyke

(2019): Mining Rank Data. In: ACM Transactions on Knowledge Discovery from Data, Vol. 13, No. 6: pp. 1-36

Hetzer, Alexander; Wever, Marcel

; Mohr, Felix and Hüllermeier, Eyke

(2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. ECDA 2019: European Conference on Data Analysis, Bayreuth, Germany, 18 - 20 March 2019. In: ECDA 2019: Book of Abstracts. pp. 65-66

Hüllermeier, Eyke

; Destercke, Sébastien and Couso, Ines (2019): Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants. In: Ben Amor, Nahla; Quost, Benjamin and Theobald, Martin (eds.) : Scalable Uncertainty Management. Lecture Notes in Computer Science, Vol. 11940. Cham: Springer. pp. 266-279

Melnikov, Vitalik and Hüllermeier, Eyke

(2019): Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for OWA. 11th Asian Conference on Machine Learning (ACML 2019), Nagoya, Japan, November 17 - 19, 2019. In: Wee Sun, Lee and Suzuki, Taiji (eds.) : Proceedings of The Eleventh Asian Conference on Machine Learning. Vol. 101. pp. 1110-1125

Merten, Marie-Luis; Seemann, Nina and Wever, Marcel Dominik

(December 2019): Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. In: Jahrbuch des Vereins für Niederdeutsche Sprachforschung = Niederdeutsches Jahrbuch, Vol. 142: pp. 124-146

Mohr, Felix; Wever, Marcel

; Tornede, Alexander

and Hüllermeier, Eyke

(September 2019): From Automated to On-The-Fly Machine Learning. In: David, Klaus; Geihs, Kurt; Lange, Martin and Stumme, Gerd (eds.) : (2021): Informatik 2019. 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, Konferenzbeiträge, der 49. Jahrestagung der Gesellschaft für Informatik, 23.- 26.9.2019 Kassel, Deutschland. Bonn: Gesellschaft für Informatik. pp. 273-274

Nguyen, Vu-Linh; Destercke, Sébastien and Hüllermeier, Eyke

(2019): Epistemic Uncertainty Sampling. In: Kralj Novak, Petra; Šmuc, Tomislav and Džeroski, Sašo (eds.) : Discovery Science. 22nd International Conference, DS 2019, Split, Croatia, October 28–30, 2019, Proceedings. Lecture Notes in Computer Science, Vol. 11828. pp. 72-86

Tagne, Vidal Kamdem; Fotso, Siméon; Fono, Louis Aimé and Hüllermeier, Eyke

(2019): Choice Functions Generated by Mallows and Plackett–Luce Relations. In: New Mathematics and Natural Computation, Vol. 15, No. 2: pp. 191-213

Tornede, Alexander

; Wever, Marcel

and Hüllermeier, Eyke

(November 2019): Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad Ranking. , KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146. 29. Workshop Computational Intelligence, Dortmund, Germany, 28. - 29. November 2019. In: Hoffmann, Frank; Hüllermeier, Eyke and Mikut, Ralf (eds.) : Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019. Karlsruhe: KIT Scientific Publishing. pp. 135-146

Waegeman, Willem

; Dembczyński, Krzysztof and Hüllermeier, Eyke

(2019): Multi-target prediction: a unifying view on problems and methods. In: Data Mining and Knowledge Discovery, Vol. 33, No. 2: pp. 293-324

Wever, Marcel

; Mohr, Felix; Hüllermeier, Eyke

and Hetzer, Alexander (2019): Towards Automated Machine Learning for Multi-Label Classification. ECDA 2019: European Conference on Data Analysis, Bayreuth, Germany, 18 - 20 March 2019. In: ECDA 2019: Book of Abstracts. p. 65

Wever, Marcel Dominik

; Mohr, Felix; Tornede, Alexander

and Hüllermeier, Eyke

(2019): Automating Multi-Label Classification Extending ML-Plan. 6th ICML Workshop on Automated Machine Learning (AutoML 2019), Long Beach, California, USA, June 14th 2019. In:

2018

Abiteboul, Serge; Arenas, Marcelo; Barceló, Pablo; Bienvenu, Meghyn; Calvanese, Diego; David, Claire; Hull, Richard; Hüllermeier, Eyke

; Kimelfeld, Benny; Libkin, Leonid; Martens, Wim; Milo, Tova; Murlak, Filip; Neven, Frank; Ortiz, Magdalena; Schwentick, Thomas; Stoyanovich, Julia; Su, Jianwen; Suciu, Dan; Vianu, Victor and Yi, Ke (April 2018): Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In: Dagstuhl Manifestos, Vol. 7, No. 1: pp. 1-29

Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke

(2018): Learning to rank based on analogical reasoning. 32nd AAAI Conference on Artificial Intelligence, New Orleans, Louisianna, USA, 2-7 February 2018. In: AAAI'18/IAAI'18/EAAI'18: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence and Thirtieth Innovative Applications of Artificial Intelligence Conference and Eighth AAAI Proceedings of the Thirty-Second AAAI Conference on Artificial. AAAI Press: Palo Alto, California, USA. pp. 2951-2958

Bengs, Viktor (2018): Confidence sets for change-point problems in nonparametric regression. Dissertation, Philipps-Universität Marburg

El Mesaoudi-Paul, Adil; Hüllermeier, Eyke

and Busa-Fekete, Róbert (2018): Ranking Distributions based on Noisy Sorting. 35th International Conference on Machine Learning, Stockholm, Sweden, 10-15 July 2018. In: Dy, Jennifer and Krause, Andreas (eds.) : Proceedings of the 35th International Conference on Machine Learning. ICML 2018. Vol. 80. pp. 3472-3480

Hesse, Michael; Timmermann, Julia; Hüllermeier, Eyke

and Trächtler, Ansgar (2018): A Reinforcement Learning Strategy for the Swing-Up of the Double Pendulum on a Cart. In: Procedia Manufacturing, Vol. 24: pp. 15-20

Melnikov, Vitalik and Hüllermeier, Eyke

(2018): On the effectiveness of heuristics for learning nested dichotomies: an empirical analysis. In: Machine Learning, Vol. 107, No. 8-10: pp. 1537-1560

Mencía, Eneldo Loza; Fürnkranz, Johannes; Hüllermeier, Eyke

and Rapp, Michael

(2018): Learning Interpretable Rules for Multi-Label Classification. In: Jair Escalante, Hugo; Escalera, Sergio; Guyon, Isabelle; Baró, Xavier; Güçlütürk, Yağmur; Güçlü, Umut and Gerven, Marcel van (eds.) : Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning, Cham: Springer. pp. 81-113

Mohr, Felix; Hüllermeier, Eyke

and Wever, Marcel

(July 2018): ML-Plan for Unlimited-Length Machine Learning Pipelines. ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018-07-10 – 2018-07-15. In:

Mohr, Felix; Lettmann, Theodor; Hüllermeier, Eyke

and Wever, Marcel Dominik

(June 2018): Programmatic Task Network Planning. 28th International Conference on Automated Planning and Scheduling, Delft, Netherlands, 2018-06-24 – 2018-06-29. In: Bercher, Pascal; Höller, Daniel; Biundo, Susanne and Alford, Ron (eds.) : Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. pp. 31-39

Mohr, Felix; Wever, Marcel

and Hüllermeier, Eyke

(2018): ML-Plan: Automated machine learning via hierarchical planning. In: Machine Learning, Vol. 107, No. 8-10: pp. 1495-1515

Mohr, Felix; Wever, Marcel

and Hüllermeier, Eyke

(2018): On-the-Fly Service Construction with Prototypes. 2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2-7 July 2018. In: 2018 IEEE International Conference on Services Computing (SCC). Piscataway, NJ: IEEE. pp. 225-232

Mohr, Felix; Wever, Marcel

and Hüllermeier, Eyke

(2018): Reduction Stumps for Multi-class Classification. In: Duivesteijn, Wouter; Siebes, Arno and Ukkonen, Antti (eds.) : (2020): Advances in Intelligent Data Analysis XVII. 17th International Symposium, IDA 2018, ’s-Hertogenbosch, The Netherlands, October 24–26, 2018, Proceedings. Lecture Notes in Computer Science, Vol. 11191. Cham: Springer. pp. 225-237

Mohr, Felix; Wever, Marcel

; Hüllermeier, Eyke

and Faez, Amin (2018): (WIP) Towards the Automated Composition of Machine Learning Services. 2018 IEEE International Conference on Services Computing (SCC), San Francisco, CA, USA, 2-7 July 2018. In: 2018 IEEE International Conference on Services Computing : IEEE SCC 2018 : part of the 2018 IEEE World Congress on Services : 2-7 July 2018, San Francisco, California, USA : proceedings. Piscataway, NJ: IEEE. pp. 241-244

Mohr, Felix; Wever, Marcel Dominik

and Hüllermeier, Eyke

(2018): Automated Machine Learning Service Composition.

Nguyen, Vu-Linh; Destercke, Sébastien; Masson, Marie-Hélène and Hüllermeier, Eyke

(2018): Reliable Multi-class Classification based on Pairwise Epistemic and Aleatoric Uncertainty. Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 13-19 July 2018. In: Lang, Jérôme (ed.) : Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Menlo Park, County of San Mateo, California: pp. 5089-5095

Schäfer, Dirk and Hüllermeier, Eyke

(2018): Dyad ranking using Plackett–Luce models based on joint feature representations. In: Machine Learning, Vol. 107, No. 5: pp. 903-941

Schäfer, Dirk and Hüllermeier, Eyke

(2018): Preference-Based Reinforcement Learning Using Dyad Ranking. In: Soldatova, Larisa; Vanschoren, Joaquin; Papadopoulos, George and Ceci, Michelangelo (eds.) : Discovery Science. Discovery Science 21st International Conference, DS 2018, Limassol, Cyprus, October 29–31, 2018, Proceedings. Lecture Notes in Computer Science, Vol. 11198. Cham: Springer. pp. 161-175

Seemann, Nina; Geierhos, Michaela; Merten, Marie-Luis; Tophinke, Doris; Wever, Marcel Dominik

and Hüllermeier, Eyke

(March 2018): Supporting the Cognitive Process in Annotation Tasks. 40. Jahrestagung der Deutschen Gesellschaft für Sprachwissenschaft, Stuttgart, Germany, 2018-03-07 – 2018-03-09. In:

Wever, Marcel

; Mohr, Felix and Hüllermeier, Eyke

(2018): Ensembles of evolved nested dichotomies for classification. GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, July 15 - 19, 2018. In: Proceedings of the Genetic and Evolutionary Computation Conference. New York, NY: Association for Computing Machinery. pp. 561-568

2017

Mikut, Ralf; Hüllermeier, Eyke

and Hoffmann, Frank (eds.) (2017): Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. Karlsruhe: KIT Scientific Publishing. [PDF, 11MB]

Ahmadi Fahandar, Mohsen and Hüllermeier, Eyke

(2017): Learning to Rank based on Analogical Reasoning.

Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke

and Couso, Inés (August 2017): Statistical Inference for Incomplete Ranking Data: The Case of Rank-Dependent Coarsening. 34th International Conference on Machine Learning, Sydney, Australia. In: Precup, Doina and Teh, Yee Whye (eds.) : Proceedings of the 34 th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017. Vol. 70. [PDF, 269kB]

Bräuning, Michael; Hüllermeier, Eyke

; Keller, Tobias and Glaum, Martin (2017): Lexicographic preferences for predictive modeling of human decision making: A new machine learning method with an application in accounting. In: European Journal of Operational Research, Vol. 258, No. 1: pp. 295-306

Couso, Inés; Dubois, Didier and Hüllermeier, Eyke

(2017): Maximum Likelihood Estimation and Coarse Data. In: Moral, Serafín; Pivert, Olivier; Sánchez, Daniel and Marín, Nicolás (eds.) : Scalable Uncertainty Management. 11th International Conference, SUM 2017, Granada, Spain, October 4-6, 2017, Proceedings. Lecture Notes in Computer Science, Vol. 10564. Cham: Springer. pp. 3-16

Couso, Inés and Hüllermeier, Eyke

(2017): Statistical Inference for Incomplete Ranking Data: A Comparison of Two Likelihood-Based Estimators. In: Frontiers in Computational Intelligence. Studies in Computational Intelligence, Vol. 739. Cham: Springer. pp. 31-46

Czech, Mike; Hüllermeier, Eyke

; Jakobs, Marie-Christine and Wehrheim, Heike (2017): Predicting rankings of software verification tools. 3rd ACM SIGSOFT International Workshop on Software Analytics, Paderborn, Germany, 4 September 2017. In: Baysal, Olga (ed.) : Proceedings of the 3rd ACM SIGSOFT International Workshop on Software Analytics. ACM: New York, NY. pp. 23-26

Ewerth, Ralph; Springstein, Matthias; Müller, Eric; Balz, Alexander; Gehlhaar, Jan; Naziyok, Tolga; Dembczyński, Krzysztof and Hüllermeier, Eyke

(2017): Estimating relative depth in single images via rankboost. 2017 IEEE International Conference on Multimedia and Expo (ICME), Hong Kong, 10-14 July 2017. In: 2017 IEEE International Conference on Multimedia and Expo (ICME) : Hong Kong, 10-14 July 2017. Piscataway, NJ: IEEE. pp. 919-924

Fürnkranz, Johannes and Hüllermeier, Eyke

(2017): Preference Learning. In: Sammut, Claude and Webb, Geoffrey I. (eds.) : Encyclopedia of Machine Learning and Data Mining. 2017 Edition. Boston, MA: Springer. pp. 1000-1005

Hüllermeier, Eyke

(2017): From knowledge-based to data-driven modeling of fuzzy rule-based systems: A critical reflection. arxiv.org

Melnikov, Vitalik and Hüllermeier, Eyke

(November 2017): Optimizing the Structure of Nested Dichotomies: A Comparison of Two Heuristics. In: Mikut, Ralf; Hüllermeier, Eyke and Hoffmann, Frank (eds.) : Proceedings. 27. Workshop Computational Intelligence, Dortmund, 23. - 24. November 2017. Karlsruhe: KIT Scientific Publishing. pp. 1-12

Mohr, Felix; Lettmann, Theo and Hüllermeier, Eyke

(2017): Planning with Independent Task Networks. In: Kern-Isberner, Gabriele; Fürnkranz, Johannes and Thimm, Matthias (eds.) : KI 2017: Advances in Artificial Intelligence. 40th Annual German Conference on AI, Dortmund, Germany, September 25–29, 2017, Proceedings. Lecture Notes in Computer Science, Vol. 10505. Cham: Springer. pp. 193-206

Platenius, Marie C.; Shaker, Ammar; Becker, Matthias; Hüllermeier, Eyke

and Schafer, Wilhelm (2017): Imprecise Matching of Requirements Specifications for Software Services Using Fuzzy Logic. In: IEEE Transactions on Software Engineering, Vol. 43, No. 8: pp. 739-759

Seemann, Nina; Merten, Marie-Luis; Geierhos, Michaela; Tophinke, Doris and Hüllermeier, Eyke

(August 2017): Annotation Challenges for Reconstructing the Structural Elaboration of Middle Low German. Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, Vancouver, Canada, August, 2017. In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Vancouver, Canada: Association for Computational Linguistics. pp. 40-45

Shaker, Ammar; Heldt, Waleri and Hüllermeier, Eyke

(2017): Learning TSK Fuzzy Rules from Data Streams. In: Ceci, Michelangelo; Hollmén, Jaakko; Džeroski, Sašo; Todorovsk, Ljupčo; Vens, Celine and Džeroski, Sašo (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 10535. pp. 559-574

Wever, Marcel

; van Rooijen, Lorijn and Hamann, Heiko (2017): Active coevolutionary learning of requirements specifications from examples. GECCO '17: Genetic and Evolutionary Computation Conference, Berlin, Germany, July 15 - 19, 2017. In: GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference July 2017. New York, NY: Association for Computing Machinery. pp. 1327-1334

Wever, Marcel Dominik

(March 2017): Active Learning of User Requirements Specifications in Dynamic Software Service Markets. Master Thesis, Universität Paderborn

Wever, Marcel Dominik

; Mohr, Felix and Hüllermeier, Eyke

(2017): Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization. 27th Workshop Computational Intelligence, Dortmund, 23. – 24. November 2017. In: Hoffmann, F.; Hüllermeier, Eyke and Mikut, R. (eds.) : Proceedings. 27. Workshop Computational Intelligence Dortmund, 23. – 24. November 2017. Karlsruhe: KIT Scientific Publishing. pp. 149-166

2016

Hoffmann, Frank; Hüllermeier, Eyke

and Mikut, Ralf (eds.) (2016): Proceedings. 26. Workshop Computational Intelligence, Dortmund, 24. - 25. November 2016. Karlsruhe: KIT Scientific Publishing. [PDF, 14MB]

Abiteboul, Serge; Arenas, Marcelo; Barceló, Pablo; Bienvenu, Meghyn; Calvanese, Diego; David, Claire; Hull, Richard; Hüllermeier, Eyke

; Kimelfeld, Benny; Libkin, Leonid; Martens, Wim; Milo, Tova; Murlak, Filip; Neven, Frank; Ortiz, Magdalena; Schwentick, Thomas; Stoyanovich, Julia; Su, Jianwen; Suciu, Dan; Vianu, Victor and Yi, Ke (December 2016): Research Directions for Principles of Data Management (Abridged). In: SIGMOD Record, Vol. 45, No. 4: 5–17

Bräuning, Michael and Hüllermeier, Eyke

(May 2016): Learning Conditional Lexicographic Preference Tree. In: Archives of data science. Series A, Vol. 1, No. 1: pp. 41-55

Dembczyński, Krzysztof; Kotłowski, Wojciech; Waegeman, Willem; Busa-Fekete, Róbert and Hüllermeier, Eyke

(2016): Consistency of Probabilistic Classifier Trees. In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe and Vreeken, Jilles (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 9852. Cham: Springer. pp. 511-526

Fürnkranz, Johannes and Hüllermeier, Eyke

(2016): Preference Learning. In: Phung, Dinh; Webb, Geoffrey I. and Sammut, Claude (eds.) : Encyclopedia of Machine Learning and Data Mining. Living Edition. New York, NY: Springer. pp. 1-7

Jasinska, Kalina; Dembczynski, Krzysztof; Busa-Fekete, Róbert; Pfannschmidt, Karlson; Klerx, Timo and Hüllermeier, Eyke

(2016): Extreme F-measure maximization using sparse probability estimates. ICML'16: 33rd International Conference on International Conference on Machine Learning, New York, NY USA, June 19 - 24, 2016. In: Balcan, Maria Florina and Weinberger, Kilian Q. (eds.) : Proceedings of the 33rd International Conference on International Conference on Machine Learning. Vol. 22. New York: Association for Computing Machinery. pp. 1435-1444

Leinweber, Matthias; Fober, Thomas; Strickert, Marc; Baumgartner, Lars; Klebe, Gerhard; Freisleben, Bernd and Hüllermeier, Eyke

(2016): CavSimBase: A Database for Large Scale Comparison of Protein Binding Sites. In: IEEE Transactions on Knowledge and Data Engineering, Vol. 28, No. 6: pp. 1423-1434

Melnikov, Vitalik and Hüllermeier, Eyke

(2016): Learning to Aggregate Using Uninorms. In: Frasconi, Paolo; Landwehr, Niels; Manco, Giuseppe and Vreeken, Jilles (eds.) : Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science, Vol. 9852. pp. 756-771

Melnikov, Vitalik; Hüllermeier, Eyke

; Kaimann, Daniel; Frick, Bernd and Gupta, Pritha (2016): Pairwise versus Pointwise Ranking: A Case Study. In: Schedae Informaticae, Vol. 25: pp. 73-83

Pfannschmidt, Karlson; Hüllermeier, Eyke

; Held, Susanne and Neiger, Reto (2016): Evaluating Tests in Medical Diagnosis: Combining Machine Learning with Game-Theoretical Concepts. In: Carvalho, Joao Paulo; Lesot, Marie-Jeanne; Kaymak, Uzay; Vieira, Susana; Bouchon-Meunier, Bernadette and Yager, Ronald R. (eds.) : Information Processing and Management of Uncertainty in Knowledge-Based Systems. 16th International Conference, IPMU 2016, Eindhoven, The Netherlands, June 20-24, 2016, Proceedings, Part I. Communications in Computer and Information Science, Vol. 610. Cham: Springer. pp. 450-461

Riemenschneider, Mona; Senge, Robin; Neumann, Ursula; Hüllermeier, Eyke

and Heider, Dominik (2016): Exploiting HIV-1 protease and reverse transcriptase cross-resistance information for improved drug resistance prediction by means of multi-label classification. In: BioData Mining, Vol. 9, 10

Schäfer, Dirk and Hüllermeier, Eyke

(2016): Plackett-Luce Networks for Dyad Ranking. LWDA 2016: Lernen, Wissen, Daten, Analysen 2016, Potsdam, Germany, September 12-14, 2016. In: Krestel, Ralf; Mottin, Davide and Müller, Emmanuel (eds.) : Proceedings of the Conference "Lernen, Wissen, Daten, Analysen". Vol. 22. Vol. 1670. pp. 323-334

Shabani, Aulon; Paul, Adil; Platon, Radu and Hüllermeier, Eyke

(2016): Predicting the Electricity Consumption of Buildings: An Improved CBR Approach. In: Goel, Ashok; Díaz-Agudo, M. Belén and Roth-Berghofer, Thomas (eds.) : Case-Based Reasoning Research and Development. Lecture Notes in Computer Science, Vol. 9969. Cham: Springer. pp. 356-369

2015

Hoffmann, Frank and Hüllermeier, Eyke

(eds.) (2015): Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Vol. 54. Karlsruhe: KIT Scientific Publishing. [PDF, 18MB]

Hüllermeier, Eyke

and Minor, Mirjam (eds.) (2015): Case-Based Reasoning Research and Development. 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015. Proceedings. Lecture Notes in Computer Science, Vol. 9343. Cham: Springer.

Abdel-Aziz, Amira and Hüllermeier, Eyke

(2015): Case Base Maintenance in Preference-Based CBR. In: Hüllermeier, Eyke and Minor, Mirjam (eds.) : Case-Based Reasoning Research and Development. 23rd International Conference, ICCBR 2015, Frankfurt am Main, Germany, September 28-30, 2015. Proceedings. Lecture Notes in Computer Science, Vol. 9343. Cham: Springer. pp. 1-14

Ewerth, Ralph; Balz, Alexander; Gehlhaar, Jan; Dembczyński, Krzysztof and Hüllermeier, Eyke

(November 2015): Depth Estimation in Monocular Images: Quantitative versus Qualitative Approaches. In: Hoffmann, Frank and Hüllermeier, Eyke (eds.) : Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Vol. 54. Karlsruhe: KIT Scientific Publishing. pp. 235-239

Garcia-Jimenez, Santiago; Bustince, Humberto; Hüllermeier, Eyke

; Mesiar, Radko; Pal, Nikhil R. and Pradera, Ana (2015): Overlap Indices: Construction of and Application to Interpolative Fuzzy Systems. In: IEEE Transactions on Fuzzy Systems, Vol. 23, No. 4: pp. 1259-1273

Henzgen, Sascha and Hüllermeier, Eyke

(2015): Weighted Rank Correlation: A Flexible Approach Based on Fuzzy Order Relations. In: Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 9285. pp. 422-437

Hüllermeier, Eyke

(2015): Does machine learning need fuzzy logic? In: Fuzzy Sets and Systems, Vol. 281: pp. 292-299

Hüllermeier, Eyke

(2015): From knowledge-based to data-driven fuzzy modeling. In: Informatik-Spektrum, Vol. 38, No. 6: pp. 500-509

Hüllermeier, Eyke

and Cheng, Weiwei (2015): Superset Learning Based on Generalized Loss Minimization. In: Machine Learning and Knowledge Discovery in Databases.European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 9285. Cham: Springer. pp. 260-275

Lu, Shenzhen and Hüllermeier, Eyke

(November 2015): Locally weighted regression through data imprecisiation. In: Hoffmann, Frank and Hüllermeier, Eyke (eds.) : Proceedings. 25. Workshop Computational Intelligence, Dortmund, 26. - 27. November 2015. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Vol. 54. Karlsruhe: KIT Scientific Publishing. pp. 97-103

Paul, Adil and Hüllermeier, Eyke

(2015): A CBR Approach to the Angry Birds Game. Twenty-Third International Conference on Case-Based Reasoning (ICCBR 2015), Frankfurt, Germany, September 28-30, 2015. In: Kendall-Morwick, Joseph (ed.) : Workshop Proceedings from The Twenty-Third International Conference on Case-Based Reasoning (ICCBR 2015). pp. 68-77

Schäfer, Dirk and Hüllermeier, Eyke

(2015): Dyad Ranking Using A Bilinear Plackett-Luce Model. In: Appice, Annalisa; Pereira Rodrigues, Pedro; Santos Costa, Vítor; Gama, João; Jorge, Alípio and Soares, Carlos (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science, Vol. 9285. Cham: Springer. pp. 227-242

Schäfer, Dirk and Hüllermeier, Eyke

(2015): Preference-based meta-learning using dyad ranking: recommending algorithms in cold-start situations. MetaSel'15: International Conference on Meta-Learning and Algorithm Selection, Porto Portugal, 7 September 2015. In: Vanschoren, Joaquin; Brazdil, Pavel and Giraud-Carrier, Christophe (eds.) : MetaSel'15: Proceedings of the 2015 International Conference on Meta-Learning and Algorithm Selection. Vol. 1455. CEUR-WS.org. pp. 110-111

Senge, Robin and Hüllermeier, Eyke

(2015): Fast Fuzzy Pattern Tree Learning for Classification. In: IEEE Transactions on Fuzzy Systems, Vol. 23, No. 6: pp. 2024-2033

Shaker, Ammar and Hüllermeier, Eyke

(2015): Recovery analysis for adaptive learning from non-stationary data streams: Experimental design and case study. In: Neurocomputing, Vol. 150, No. A: pp. 250-264

Szörényi, Baiÿzs; Busa-Fekete, Róbert; Paul, Adil and Hüllermeier, Eyke

(2015): Online F-measure optimization. NIPS'15: 28th International Conference on Neural Information Processing Systems, Montreal Canada, December 7 - 12, 2015. In: Cortes, C.; Lee, D. D.; Sugiyama, M. and Garnett, R. (eds.) : NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems. Vol. 1. Cambridge, MA: MIT Press. pp. 595-603

Szörényi, Baiÿzs; Busa-Fekete, Róbert; Paul, Adil and Hüllermeier, Eyke

(2015): Online rank elicitation for Plackett-Luce: a dueling bandits approach. NIPS'15: 28th International Conference on Neural Information Processing Systems, Montreal Canada, December 7 - 12, 2015. In: Cortes, C.; Lee, D. D.; Sugiyama, M. and Garnett, R. (eds.) : NIPS'15: Proceedings of the 28th International Conference on Neural Information Processing Systems. Vol. 1. Cambridge, MA: MIT Press. pp. 604-612

Szörényi, Balázs; Busa-Fekete, Róbert; Weng, Paul and Hüllermeier, Eyke

(2015): Qualitative Multi-Armed Bandits: A Quantile-Based Approach. 32nd International Conference on Machine Learning, Lille, France, July 6 - 11, 2015. In: Proceedings of the 32nd International Conference on Machine Learning. Vol. 37. pp. 1660-1668

2014

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

and Meo, Rosa (eds.) (2014): Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part I. Lecture Notes in Computer Science, Vol. 8724. Berlin, Heidelberg: Springer.

Abdel-Aziz, Amira; Strickert, Marc and Hüllermeier, Eyke

(2014): Learning Solution Similarity in Preference-Based CBR. In: Lamontagne, Luc and Plaza, Enric (eds.) : Case-Based Reasoning Research and Development. 22nd International Conference, ICCBR 2014, Cork, Ireland, September 29, 2014 - October 1, 2014. Proceedings. Lecture Notes in Computer Science, Vol. 8765. Cham: Springer. pp. 17-31

Agarwal, Manish; Fallah Tehrani, Ali and Hüllermeier, Eyke

(2014): Preference-based Learning of Ideal Solutions in TOPSIS-like Decision Models. In: Journal of Multi-Criteria Decision Analysis, Vol. 22, No. 3-4: pp. 175-183

Busa-Fekete, Róbert; Hüllermeier, Eyke

and Szörényi, Balázs (2014): Preference-Based Rank Elicitation using Statistical Models: The Case of Mallows. 31st International Conference on Machine Learning (ICML 2014), Beijing, China, 21-26 June, 2014. In: Xing, Eric P. and Jebara, Tony (eds.) : Proceedings of the 31st International Conference on Machine Learning. Vol. 32. Bejing, China: PMLR. pp. 1071-1079

Busa-Fekete, Róbert and Hüllermeier, Eyke

(2014): A Survey of Preference-Based Online Learning with Bandit Algorithms. In: Auer, Peter; Clark, Alexander; Zeugmann, Thomas and Zilles, Sandra (eds.) : Algorithmic Learning Theory. 25th International Conference, ALT 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science, Vol. 8776. Cham: Springer. pp. 18-39

Busa-Fekete, Róbert; Szörényi, Balázs and Hüllermeier, Eyke

(July 2014): PAC Rank Elicitation through Adaptive Sampling of Stochastic Pairwise Preferences. 28th AAAI Conference on Artificial Intelligence (AAAI-14), Quebec City, Canada, Jul 2014. In:

Busa-Fekete, Róbert; Szörényi, Balázs; Weng, Paul; Cheng, Weiwei and Hüllermeier, Eyke

(2014): Preference-based reinforcement learning: evolutionary direct policy search using a preference-based racing algorithm. In: Machine Learning, Vol. 97, No. 3: pp. 327-351

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

and Meo, Rosa (2014): Guest Editors’ introduction: special issue of the ECML/PKDD 2014 journal track. In: Machine Learning, Vol. 97, No. 1-2: pp. 1-3

Calders, Toon; Esposito, Floriana; Hüllermeier, Eyke

and Meo, Rosa (2014): Guest editors’ introduction: special issue of the ECML/PKDD 2014 journal track. In: Data Mining and Knowledge Discovery, Vol. 28, No. 5-6: pp. 1129-1133

Donner-Banzhoff, Norbert; Haasenritter, Jörg; Hüllermeier, Eyke

; Viniol, Annika; Bösner, Stefan and Becker, Annette (2014): The comprehensive diagnostic study is suggested as a design to model the diagnostic process. In: Journal of Clinical Epidemiology, Vol. 67, No. 2: pp. 124-132 [PDF, 1MB]

Fallah Tehrani, Ali; Strickert, Marc and Hüllermeier, Eyke

(2014): The Choquet kernel for monotone data. 22nd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges (Belgium), 23-25 April 2014. In: Verleysen, Michel (ed.) : ESANN 2014 proceedings. Ciaco: Louvain-la-Neuve. pp. 337-342

Fürnkranz, Johannes; Hüllermeier, Eyke

; Rudin, Cynthia; Slowinski, Roman and Sanner, Scott (eds.) (2014): Preference Learning. Report from Dagstuhl Seminar 14101. Dagstuhl Reports, Vol. 4. Dagstuhl: Schloss Dagstuhl, Leibniz-Zentrum fuer Informatik. [PDF, 1MB]

Henzgen, Sascha and Hüllermeier, Eyke

(2014): Mining Rank Data. In: Džeroski, Sašo; Panov, Panče; Kocev, Dragi and Todorovski, Ljupčo (eds.) : Discovery Science. 17th International Conference, DS 2014, Bled, Slovenia, October 8-10, 2014. Proceedings. Lecture Notes in Computer Science, Vol. 8777. Cham: Springer. pp. 123-134

Henzgen, Sascha; Strickert, Marc and Hüllermeier, Eyke

(2014): Visualization of evolving fuzzy rule-based systems. In: Evolving Systems, Vol. 5, No. 3: pp. 175-191

Hüllermeier, Eyke

(2014): Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization. In: International Journal of Approximate Reasoning, Vol. 55, No. 7: pp. 1519-1534

Hüllermeier, Eyke

(2014): Rejoinder on “Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization”. In: International Journal of Approximate Reasoning, Vol. 55, No. 7: pp. 1609-1613

Krempl, Georg; Žliobaite, Indre; Brzeziński, Dariusz; Hüllermeier, Eyke

; Last, Mark; Lemaire, Vincent; Noack, Tino; Shaker, Ammar; Sievi, Sonja; Spiliopoulou, Myra and Stefanowski, Jerzy (2014): Open challenges for data stream mining research. In: ACM SIGKDD Explorations Newsletter, Vol. 16, No. 1: pp. 1-10

Krotzky, Timo; Fober, Thomas; Hüllermeier, Eyke

and Klebe, Gerhard (2014): Extended Graph-Based Models for Enhanced Similarity Search in Cavbase. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 11, No. 5: pp. 878-890

Mernberger, Marco; Moog, Daniel; Stork, Simone; Zauner, Stefan; Maier, Uwe G. and Hüllermeier, Eyke

(2014): Protein Sub-Cellular Localization Prediction for Special Compartments via Optimized Time Series Distances. In: Journal of Bioinformatics and Computational Biology, Vol. 12, No. 1: p. 1350016

Montañes, Elena; Senge, Robin; Barranquero, Jose; Ramón Quevedo, José; José del Coz, Juan and Hüllermeier, Eyke

(2014): Dependent binary relevance models for multi-label classification. In: Pattern Recognition, Vol. 47, No. 3: pp. 1494-1508

Senge, Robin; Bösner, Stefan; Dembczyński, Krzysztof; Haasenritter, Jörg; Hirsch, Oliver; Donner-Banzhoff, Norbert and Hüllermeier, Eyke

(2014): Reliable classification: Learning classifiers that distinguish aleatoric and epistemic uncertainty. In: Information Sciences, Vol. 255: pp. 16-29

Shaker, Ammar and Hüllermeier, Eyke

(2014): Survival analysis on data streams: Analyzing temporal events in dynamically changing environments. In: International Journal of Applied Mathematics and Computer Science, Vol. 24, No. 1: pp. 199-212 [PDF, 506kB]

Stock, Michiel; Fober, Thomas; Hüllermeier, Eyke

; Glinca, Serghei; Klebe, Gerhard; Pahikkala, Tapio; Airola, Antti; De Baets, Bernard and Waegeman, Willem (2014): Identification of Functionally Related Enzymes by Learning-to-Rank Methods. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 11, No. 6: pp. 1157-1169

Strickert, Marc; Bunte, Kerstin; Schleif, Frank-Michael and Hüllermeier, Eyke

(2014): Correlation-based embedding of pairwise score data. In: Neurocomputing, Vol. 141: pp. 97-109

Waegeman, Willem

; Dembczyński, Krzysztof; Jachnik, Arkadiusz; Cheng, Weiwei and Hüllermeier, Eyke

(November 2014): On the Bayes-Optimality of F-Measure Maximizers. In: Journal of Machine Learning Research, Vol. 15, 103: pp. 3513-3568

2013

Hoffmann, Frank and Hüllermeier, Eyke

(eds.) (2013): Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Vol. 46. Karlsruhe: KIT Scientific Publishing. [PDF, 18MB]

Busa-Fekete, Róbert; Szörényi, Balázs; Cheng, Weiwei; Weng, Paul and Hüllermeier, Eyke

(2013): Top-k Selection based on Adaptive Sampling of Noisy Preferences. ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013. In: Dasgupta, Sanjoy and McAllester, David (eds.) : Proceedings of the 30th International Conference on International Conference on Machine Learning. Vol. 28. pp. 1094-1102

Bösner, Stefan; Bönisch, Katharina; Haasenritter, Jörg; Schlegel, Patrice; Hüllermeier, Eyke

and Donner-Banzhoff, Norbert (2013): Chest pain in primary care: is the localization of pain diagnostically helpful in the critical evaluation of patients? - A cross sectional study. In: BMC Family Practice, Vol. 14, 154 [PDF, 1MB]

Cheng, Weiwei; Henzgen, Sascha and Hüllermeier, Eyke

(2013): Labelwise versus Pairwise Decomposition in Label Ranking. LWA 2013. Lernen, Wissen & Adaptivität, Bamberg, Germany, 7.-9. October 2013. In: Henrich, Andreas and Sperker, Hans-Christian (eds.) : LWA 2013. Lernen, Wissen & Adaptivität, Workshop Proceedings Bamberg, 7.-9. October 2013. Dagstuhl Reports, Vol. 4. Bamberg: Universitätsbibliothek Bamberg. pp. 129-136

Cheng, Weiwei and Hüllermeier, Eyke

(2013): A Nearest Neighbor Approach to Label Ranking based on Generalized Labelwise Loss Minimization. 7th Multidisciplinary Workshop on Advances in Preference Handling (M-PREF '13), Beijing, China, August 3-5, 2013. In:

Dembczyński, Krzysztof; Jachnik, Arkadiusz; Kotlowski, Wojciech; Waegeman, Willem and Hüllermeier, Eyke

(2013): Optimizing the F-Measure in Multi-Label Classification: Plug-in Rule Approach versus Structured Loss Minimization. ICML'13: 30th International Conference on International Conference on Machine Learning, Atlanta GA USA, June 16 - 21, 2013. In: Dasgupta, Sanjoy and McAllester, David (eds.) : Proceedings of the 30th International Conference on Machine Learning. Vol. 28. pp. 1030-1038

Fober, Thomas; Klebe, Gerhard and Hüllermeier, Eyke

(2013): Local Clique Merging: An Extension of the Maximum Common Subgraph Measure with Applications in Structural Bioinformatics. In: Lausen, Berthold; Poel, Dirk van den and Ultsch, Alfred (eds.) : Algorithms from and for Nature and Life. Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization, Cham: Springer. pp. 279-286

Haasenritter, Jörg; Viniol, Annika; Becker, Annette; Bösner, Stefan; Hüllermeier, Eyke

; Senge, Robin and Donner-Banzhoff, Norbert (2013): Diagnose im Kontext – eine erweiterte Perspektive. In: Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, Vol. 107, No. 9-10: pp. 585-591

Heider, Dominik; Senge, Robin; Cheng, Weiwei and Hüllermeier, Eyke

(2013): Multilabel classification for exploiting cross-resistance information in HIV-1 drug resistance prediction. In: Bioinformatics, Vol. 29, No. 16: pp. 1946-1952

Henzgen, Sascha and Hüllermeier, Eyke

(December 2013): Weighted Rank Correlation Measures Based on Fuzzy Order Relations. In: Hoffmann, Frank and Hüllermeier, Eyke (eds.) : Proceedings. 23. Workshop Computational Intelligence, Dortmund, 5. - 6. Dezember 2013. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Karlsruher Institut für Technologie, Vol. 46. Karlsruhe: KIT Scientific Publishing. pp. 227-236

Henzgen, Sascha; Strickert, Marc and Hüllermeier, Eyke

(2013): Rule Chains for Visualizing Evolving Fuzzy Rule-Based Systems. In: Burduk, Robert; Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał and Zolnierek, Andrzej (eds.) : Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, Vol. 226. pp. 279-288

Hüllermeier, Eyke

and Cheng, Weiwei (2013): Preference-Based CBR: General Ideas and Basic Principles. In: Rossi, Francesca (ed.) : (2013): Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing, China, 3 - 9 August 2013, Vol. 4. Palo Alto, Calif.: AAAI Press. pp. 3012-3016

Hüllermeier, Eyke

and Tehrani, Ali Fallah (2013): Efficient Learning of Classifiers Based on the 2-Additive Choquet Integral. In: Moewes, Christian and Nürnberger, Andreas (eds.) : Computational Intelligence in Intelligent Data Analysis. Studies in Computational Intelligence, Vol. 445. Berlin, Heidelberg: Springer. pp. 17-29

Nasiri, Maryam; Fober, Thomas; Senge, Robin and Hüllermeier, Eyke

(2013): Fuzzy pattern trees as an alternative to rule-based fuzzy systems: Knowledge-driven, data-driven and hybrid modeling of color yield in polyester dyeing. 2013 Joint IFSA World Congress and NAFIPS Annual Meeting (IFSA/NAFIPS), Edmonton, AB, Canada, 24-28 June 2013. In: Proceedings of the 2013 Joint IFSA World Congress NAFIPS Annual Meeting (IFSAI NAFIPS), Edmonton, Canada, June 24-28, 2013. Piscataway, NJ: IEEE. pp. 715-721

Senge, Robin; Del Coz, Juan José and Hüllermeier, Eyke

(2013): On the Problem of Error Propagation in Classifier Chains for Multi-label Classification. In: Spiliopoulou, Myra; Schmidt-Thieme, Lars and Janning, Ruth (eds.) : Data Analysis, Machine Learning and Knowledge Discovery. Studies in Classification, Data Analysis, and Knowledge Organization, Cham: Springer. pp. 163-170

Shaker, Ammar and Hüllermeier, Eyke

(2013): Recovery Analysis for Adaptive Learning from Non-stationary Data Streams. In: Burduk, Robert; Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał and Zolnierek, Andrzej (eds.) : Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, Vol. 226. pp. 289-298

Shaker, Ammar and Hüllermeier, Eyke

(2013): Event history analysis on data streams: An application to earthquake occurence. RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic, September 27, 2013. In: Krempl, Georg; Zliobaite, Indre; Wang, Yin and Forman, Georg (eds.) : Real-World Challenges for Data Stream Mining : Proceedings of the 1st International Workshop on Real-World Challenges for Data Stream Mining, ReaiStream 2013. Magdeburg: Universität Magdeburg. pp. 38-41

Shaker, Ammar; Senge, Robin and Hüllermeier, Eyke

(2013): Evolving fuzzy pattern trees for binary classification on data streams. In: Information Sciences, Vol. 220: pp. 34-45

Szarvas, György; Busa-Fekete, Róbert and Hüllermeier, Eyke

(2013): Learning to Rank Lexical Substitutions. 2013 Conference on Empirical Methods in Natural Language Processing, Seattle, Washington, USA, 18-21 October 2013. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing,. Association for Computational Linguistics. pp. 1926-1932

Tehrani, Ali Fallah and Hüllermeier, Eyke

(2013): Ordinal Choquistic Regression. 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2013), Milan, Italy, 11 - 13 September 2013. In: 8th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2013) : Milan, Italy, 11 - 13 September 2013. Advances in intelligent systems research, Vol. 32. Vol. 32. Red Hook, NY: Curran. pp. 802-809 [PDF, 1MB]

Weng, Paul; Busa-Fekete, Róbert and Hüllermeier, Eyke

(2013): Interactive Q-Learning with Ordinal Rewards and Unreliable Tutor. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Reinforcement Learning with Generalized Feedback, Prague, 23rd September 2013. In: pp. 1-13

Weng, Paul; Busa-Fekete, Róbert and Hüllermeier, Eyke

(2013): Preference-based Evolutionary Direct Policy Search. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013). Reinforcement Learning with Generalized Feedback, Prague, 23rd September 2013. In: pp. 1-8

2012

Bustince, Humberto; Pagola, Miguel; Mesiar, Radko; Hüllermeier, Eyke

and Herrera, Francisco (2012): Grouping, Overlap, and Generalized Bientropic Functions for Fuzzy Modeling of Pairwise Comparisons. In: IEEE Transactions on Fuzzy Systems, Vol. 20, No. 3: pp. 405-415

Cheng, Weiwei and Hüllermeier, Eyke

(2012): Probability Estimation for Multi-class Classification Based on Label Ranking. In: Flach, Peter A.; Bie, Tijl De and Cristianini, Nello (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Lecture Notes in Computer Science, Vol. 7524. Berlin, Heidelberg: Springer. pp. 83-98

Cheng, Weiwei; Hüllermeier, Eyke

; Waegeman, Willem and Welker, Volkmar (2012): Label Ranking with Partial Abstention based on Thresholded Probabilistic Models. 25. NIPS 2012, Lake Tahoe, Nevada, USA, December 3-8, 2012. In: Advances in Neural Information Processing Systems. Vol. 25. pp. 2510-2518

Dembczyński, Krzysztof; Kotłowski, Wojciech and Hüllermeier, Eyke

(June 2012): Consistent multilabel ranking through univariate loss minimization. ICML'12: 29th International Coference on International Conference on Machine Learning, Edinburgh, Scotland, UK, 26 June 2012- 1 July 2012. In: Langford, John and Pineau, Joelle (eds.) : Proceedings of the Twenty-Ninth International Conference on Machine Learning. pp. 1347-1354

Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei and Hüllermeier, Eyke

(2012): On label dependence and loss minimization in multi-label classification. In: Machine Learning, Vol. 88, No. 1-2: pp. 5-45

Dembczyński, Krzysztof; Waegeman, Willem and Hüllermeier, Eyke

(August 2012): An Analysis of Chaining in Multi-Label Classification. In: De Raedt, Luc; Bessiere, Christian; Dubois, Didier; Doherty, Patrick; Frasconi, Paolo; Heintz, Fredrik and Lucas, Peter (eds.) : ECAI 2012 : 20th European Conference on Artificial Intelligence, 27 - 31 August 2012, Montpellier, France. Frontiers in Artificial Intelligence and Applications, Vol. 242. Amsterdam: IOS Press. pp. 294-299 [PDF, 258kB]

Fober, Thomas; Mernberger, Marco; Klebe, Gerhard and Hüllermeier, Eyke

(2012): Fingerprint Kernels for Protein Structure Comparison. In: Molecular Informatics, Vol. 31, No. 6-7: pp. 443-452

Fürnkranz, Johannes and Hüllermeier, Eyke

(2012): Preference Learning. In: Seel, N. M. (ed.) : Encyclopedia of the Sciences of Learning. 2012 Edition. Boston, MA: Springer

Fürnkranz, Johannes; Hüllermeier, Eyke

; Cheng, Weiwei and Park, Sang-Hyeun (2012): Preference-based reinforcement learning: a formal framework and a policy iteration algorithm. In: Machine Learning, Vol. 89, No. 1-2: pp. 123-156

Hoffmann, Frank; Hüllermeier, Eyke

and Kroll, Andreas (2012): Ausgewählte Beiträge des GMA-Fachausschusses 5.14 “Computational Intelligence”. In: at - Automatisierungstechnik, Vol. 60, No. 10: pp. 587-588

Hüllermeier, Eyke

(2012): Fuzzy Rules in Data Mining: From Fuzzy Associations to Gradual Dependencies. In: Trillas, Enric; Bonissone, Piero P.; Magdalena, Luis and Kacprz, Janusz (eds.) : Combining Experimentation and Theory. A Hommage to Abe Mamdani. Studies in Fuzziness and Soft Computing, Vol. 271. pp. 123-135 [PDF, 11MB]

Hüllermeier, Eyke

and Fallah Tehrani, Ali (2012): On the VC-Dimension of the Choquet Integral. In: Greco, Salvatore; Bouchon-Meunier, Bernadette; Coletti, Gulianella; Fedrizzi, Mario; Matarazzo, Benedetto and Yager, Ronald R. (eds.) : Advances on Computational Intelligence. 14th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2012, Catania, Italy, July 9-13, 2012. Proceedings, Part I. Communications in Computer and Information Science, Vol. 297. Berlin, Heidelberg: Springer. pp. 42-50

Hüllermeier, Eyke

; Rifqi, Maria; Henzgen, Sascha and Senge, Robin (2012): Comparing Fuzzy Partitions: A Generalization of the Rand Index and Related Measures. In: IEEE Transactions on Fuzzy Systems, Vol. 20, No. 3: pp. 546-556

Leinweber, Matthias; Baumgärtner, Lars; Mernberger, Marco; Fober, Thomas; Hüllermeier, Eyke

; Klebe, Gerhard and Freisleben, Bernd (2012): GPU-based Cloud computing for comparing the structure of protein binding sites. 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST), Campione d'Italia, Italy, 18-20 June 2012. In: Proceedings of the 2012 6th IEEE International Conference on Digital Ecosystems and Technologies (DEST) - Complex Environment Engineering Municipal Casino, Campione d’Italia (Italy) 18 – 20 June 2012. Piscataway, NJ: IEEE. pp. 1-6

Ruiz, M. Dolores and Hüllermeier, Eyke

(2012): A formal and empirical analysis of the fuzzy gamma rank correlation coefficient. In: Information Sciences, Vol. 206: pp. 1-17

Senge, Robin; Fober, Thomas; Nasiri, Maryam and Hüllermeier, Eyke

(2012): Fuzzy Pattern Trees: Ein alternativer Ansatz zur Fuzzy-Modellierung. In: at - Automatisierungstechnik, Vol. 60, No. 10: pp. 622-629

Shaker, Ammar and Hüllermeier, Eyke

(2012): IBLStreams: a system for instance-based classification and regression on data streams. In: Evolving Systems, Vol. 3, No. 4: pp. 235-249

Tehrani, Ali Fallah; Cheng, Weiwei; Dembczyński, Krzysztof and Hüllermeier, Eyke

(2012): Learning monotone nonlinear models using the Choquet integral. In: Machine Learning, Vol. 89, No. 1-2: pp. 183-211

Tehrani, Ali Fallah; Cheng, Weiwei and Hüllermeier, Eyke

(2012): Preference Learning Using the Choquet Integral: The Case of Multipartite Ranking. In: IEEE Transactions on Fuzzy Systems, Vol. 20, No. 6: pp. 1102-1113

2011

Cheng, Weiwei; Fürnkranz, Johannes; Hüllermeier, Eyke

and Park, Sang-Hyeun (2011): Preference-Based Policy Iteration: Leveraging Preference Learning for Reinforcement Learning. In: Gunopulos, Dimitrios; Hofmann, Thomas; Malerba, Donato and Vazirgiannis, Michalis (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2011, Athens, Greece, September 5-9, 2011. Proceedings, Part I. Lecture Notes in Computer Science, Vol. 6911. Berlin, Heidelberg: Springer. pp. 312-327

Domshlak, Carmel; Hüllermeier, Eyke

; Kaci, Souhila and Prade, Henri (2011): Preferences in AI: An overview. In: Artificial Intelligence, Vol. 175, No. 7-8: pp. 1037-1052

Fober, Thomas; Glinca, Gerghei; Klebe, Gerhard and Hüllermeier, Eyke

(2011): Superposition and Alignment of Labeled Point Clouds. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 8, No. 6: pp. 1653-1666

Fürnkranz, Johannes and Hüllermeier, Eyke

(2011): Preference Learning. In: Sammut, Claude (ed.) : Encyclopedia of Machine Learning. 2010 Edition. Boston, MA: Springer. pp. 789-795

Hirsch, Oliver; Bösner, Stefan; Hüllermeier, Eyke

; Senge, Robin; Dembczynski, Krzysztof and Donner-Banzhoff, Norbert (2011): Multivariate modeling to identify patterns in clinical data: the example of chest pain. In: BMC Medical Research Methodology, Vol. 11, 155 [PDF, 3MB]

Hüllermeier, Eyke

(2011): Fuzzy sets in machine learning and data mining. In: Applied Soft Computing, Vol. 11, No. 2: pp. 1493-1505

Hüllermeier, Eyke

(2011): Fuzzy machine learning and data mining. In: WIREs: Data Mining and Knowledge Discovery, Vol. 1, No. 4: pp. 269-283

Hüllermeier, Eyke

and Fürnkranz, Johannes (2011): Learning from Label Preferences. In: Elomaa, Tapio; Hollmén, Jaakko and Mannila, Heikki (eds.) : Discovery Science. 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings. Lecture Notes in Computer Science, Vol. 6926. pp. 2-17

Hüllermeier, Eyke

and Schlegel, Patrice (September 2011): Preference-Based CBR: First Steps toward a Methodological Framework. In: Ram, Ashwin and Wiratunga, Nirmalie (eds.) : Case-Based Reasoning Research and Development. 19th International Conference on Case-Based Reasoning, ICCBR 2011, London, UK, September 12-15, 2011. Proceedings. Lecture Notes in Computer Science, Vol. 6880. Berlin, Heidelberg: Springer. pp. 77-91

Lughofer, Edwin and Hüllermeier, Eyke

(2011): On-line Redundancy Elimination in Evolving Fuzzy Regression Models using a Fuzzy Inclusion Measure. EUSFLAT - LFA 2011, Aix-Les-Bains, France, 18 - 22 July 2011. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011). Atlantis Press. pp. 380-387 [PDF, 2MB]

Mernberger, Marco; Klebe, Gerhard and Hüllermeier, Eyke

(2011): SEGA: Semiglobal Graph Alignment for Structure-Based Protein Comparison. In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 8, No. 5: pp. 1330-1343

Senge, Robin and Hüllermeier, Eyke

(2011): Top-Down Induction of Fuzzy Pattern Trees. In: IEEE Transactions on Fuzzy Systems, Vol. 19, No. 2: pp. 241-252

Tehrani, Ali Fallah; Cheng, Weiwei and Hüllermeier, Eyke

(2011): Choquistic Regression: Generalizing Logistic Regression using the Choquet Integral. EUSFLAT - LFA 2011, Aix-Les-Bains, France, 18 - 22 July 2011. In: Proceedings of the 7th conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011). Advances in Intelligent Systems Research, pp. 868-875 [PDF, 1MB]

2010

Fürnkranz, Johannes and Hüllermeier, Eyke

(eds.) (2010): Preference Learning. Berlin: Springer.

Cheng, Weiwei; Dembczyński, Krzysztof and Hüllermeier, Eyke

(2010): Graded multilabel classification: the ordinal case. ICML '10: 27th International Conference on International Conference on Machine Learning, Haifa, Israel, June 21 - 24, 2010. In: ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning. Madison, WI: Omnipress. pp. 223-230

Cheng, Weiwei; Rademaker, Michaël; De Baets, Bernard and Hüllermeier, Eyke

(2010): Predicting Partial Orders: Ranking with Abstention. In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides and Sebag, Michèle (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 6321. Berlin, Heidelberg: Springer. pp. 215-230

Dembczyński, Krzysztof; Waegeman, Willem; Cheng, Weiwei and Hüllermeier, Eyke

(2010): Regret Analysis for Performance Metrics in Multi-Label Classification: The Case of Hamming and Subset Zero-One Loss. In: Balcázar, José Luis; Bonchi, Francesco; Gionis, Aristides and Sebag, Michèle (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 6321. Berlin, Heidelberg: Springer. pp. 280-295

Fober, Thomas and Hüllermeier, Eyke

(2010): Similarity measures for protein structures based on fuzzy histogram comparison. International Conference on Fuzzy Systems, Barcelona, Spain, 18-23 July 2010. In: International Conference on Fuzzy Systems. Piscataway, NJ: IEEE. pp. 1-7

Fürnkranz, Johannes and Hüllermeier, Eyke

(2010): Preference Learning and Ranking by Pairwise Comparison. In: Fürnkranz, Johannes and Hüllermeier, Eyke (eds.) : Preference Learning. Berlin, Heidelberg: Springer. pp. 65-82

Fürnkranz, Johannes and Hüllermeier, Eyke

(2010): Preference Learning: An Introduction. In: Fürnkranz, Johannes and Hüllermeier, Eyke (eds.) : Preference Learning. Berlin: Springer. pp. 1-17

Hühn, Jens Christian and Hüllermeier, Eyke

(2010): An Analysis of the FURIA Algorithm for Fuzzy Rule Induction. In: Koronacki, Jacek; Raś, Zbigniew W.; Wierzchoń, Sławomir T. and Kacprzyk, Janusz (eds.) : Advances in Machine Learning I. Dedicated to the Memory of Professor Ryszard S. Michalski. Studies in Computational Intelligence, Vol. 262. Berlin, Heidelberg: Springer. pp. 321-344

Hüllermeier, Eyke

(2010): Uncertainty in Clustering and Classification. In: Deshpande, Amol and Hunter, Anthony (eds.) : Scalable Uncertainty Management.4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. Proceedings. Lecture Notes in Computer Science, Vol. 6379. Berlin, Heidelberg: Springer. pp. 16-19

Hüllermeier, Eyke

and Fürnkranz, Johannes (2010): On predictive accuracy and risk minimization in pairwise label ranking. In: Journal of Computer and System Sciences, Vol. 76, No. 1: pp. 49-62

Hüllermeier, Eyke

and Vanderlooy, Stijn (2010): Combining predictions in pairwise classification: An optimal adaptive voting strategy and its relation to weighted voting. In: Pattern Recognition, Vol. 43, No. 1: pp. 128-142

Koh, Hyung-Won and Hüllermeier, Eyke

(2010): Mining Gradual Dependencies Based on Fuzzy Rank Correlation. In: Borgel, Christian (ed.) : Combining Soft Computing and Statistical Methods in Data Analysis. Advances in Intelligent and Soft Computing, Vol. 77. Berlin, Heidelberg: Springer. pp. 379-386

Pfeffer, Patrick; Fober, Thomas; Hüllermeier, Eyke

and Klebe, Gerhard (2010): GARLig: A Fully Automated Tool for Subset Selection of Large Fragment Spaces via a Self-Adaptive Genetic Algorithm. In: Journal of Chemical Information and Modeling, Vol. 50, No. 9: pp. 1644-1659

Senge, Robin and Hüllermeier, Eyke

(2010): Pattern trees for regression and fuzzy systems modeling. 2010 IEEE International Conference on Fuzzy Systems (FUZZ‐IEEE 2010), Barcelona, Spain, 18-23 July 2010. In: International Conference on Fuzzy Systems (FUZZ 2010) : Barcelona, Spain, 18 - 23 July 2010. Piscataway, NJ: IEEE. pp. 1-7

2009

Hoffmann, Frank and Hüllermeier, Eyke

(eds.) (2009): Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), Vol. 29. Karlsruhe: KIT Scientific Publishing. [PDF, 19MB]

Boukhris, Imen; Elouedi, Zied; Fober, Thomas; Mernberger, Marco and Hüllermeier, Eyke

(2009): Similarity Analysis of Protein Binding Sites: A Generalization of the Maximum Common Subgraph Measure Based on Quasi-Clique Detection. 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 30 Nov.-2 Dec. 2009. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications. Piscataway, NJ: IEEE. pp. 1245-1250

Cheng, Weiwei; Hühn, Jens and Hüllermeier, Eyke

(2009): Decision tree and instance-based learning for label ranking. ICML '09: 26th Annual International Conference on Machine Learning, Montreal Quebec, Canada, June 14 - 18, 2009. In: Danyluk, Andrea (ed.) : Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY: Association for Computing Machinery. pp. 161-168

Cheng, Weiwei and Hüllermeier, Eyke

(2009): Combining instance-based learning and logistic regression for multilabel classification. In: Machine Learning, Vol. 76, No. 2-3: pp. 211-225

Cheng, Weiwei and Hüllermeier, Eyke

(2009): Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. In: Buntine, Wray; Grobelnik, Marko; Mladenić, Dunja and Shawe-Taylor, John (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 5781. Berlin, Heidelberg: Springer. p. 6

Cheng, Weiwei and Hüllermeier, Eyke

(2009): Combining Instance-Based Learning and Logistic Regression for Multilabel Classification. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. In: Hartmann, Melanie and Janssen, Frederik (eds.) : Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. pp. 22-29

Cheng, Weiwei and Hüllermeier, Eyke

(2009): A New Instance-Based Label Ranking Approach Using the Mallows Model. In: Yu, Wen; He, Haibo and Zhang, Nian (eds.) : (2009): Advances in Neural Networks – ISNN 2009 : 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26-29, 2009 Proceedings, Part I. Lecture Notes in Computer Science, Vol. 5551. Berlin, Heidelberg: Springer. pp. 707-716

Fober, Thomas; Klebe, Gerhard and Hüllermeier, Eyke

(2009): Efficient Construction of Multiple Geometrical Alignments for the Comparison of Protein Binding Sites. 2009 Ninth International Conference on Intelligent Systems Design and Applications, Pisa, Italy, 30 Nov.-2 Dec. 2009. In: 2009 Ninth International Conference on Intelligent Systems Design and Applications. Piscataway, NJ: IEEE. pp. 1251-1256

Fober, Thomas; Mernberger, Marco; Melnikov, Vitalik; Moritz, Ralph and Hüllermeier, Eyke

(2009): Extension and Empirical Comparison of Graph-Kernels for the Analysis of Protein Active Sites. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. In: Hartmann, Melanie and Janssen, Frederik (eds.) : Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. pp. 30-36

Fürnkranz, Johannes; Hüllermeier, Eyke

and Vanderlooy, Stijn (2009): Binary Decomposition Methods for Multipartite Ranking. In: Buntine, Wray; Grobelnik, Marko; Mladenić, Dunja and Shawe-Taylor, John (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 5781. Berlin, Heidelberg: Springer. pp. 359-374

Hoffmann, Frank and Hüllermeier, Eyke

(December 2009): Multiple Geometrical Alignments for the Analysis of Protein Active Sites. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009. In: Hoffmann, Frank and Hüllermeier, Eyke (eds.) : Proceedings. 19. Workshop Computational Intelligence, Dortmund, 2. - 4. Dezember 2009. Schriftenreihe des Instituts für Angewandte Informatik - Automatisierungstechnik, Universität Karlsruhe (TH), Vol. 29. Karlsruhe: KIT Scientific Publishing. pp. 237-255

Hühn, Jens and Hüllermeier, Eyke

(2009): FURIA: an algorithm for unordered fuzzy rule induction. In: Data Mining and Knowledge Discovery, Vol. 19, No. 3: pp. 293-319

Hüllermeier, Eyke

(2009): On the Usefulness of Fuzzy Sets in Data Mining. In: Views on Fuzzy Sets and Systems from Different Perspectives. Philosophy and Logic, Criticisms and Applications. Studies in Fuzziness and Soft Computing, Vol. 243. Berlin, Heidelberg: Springer. pp. 457-470

Hüllermeier, Eyke

and Vanderlooy, S. (2009): Why Fuzzy Decision Trees are Good Rankers. In: IEEE Transactions on Fuzzy Systems, Vol. 17, No. 6: pp. 1233-1244

Senge, Robin and Hüllermeier, Eyke

(2009): Learning Pattern Tree Classifiers Using a Co-Evolutionary Algorithm. LWA 2009: Workshop-Woche: Lernen, Wissen, Adaptivität, Darmstadt, Germany, 21.-23. September 2009. In: Hartmann, Melanie and Janssen, Frederik (eds.) : Darmstadt: FG Telekooperation/FG Knowledge Engineering, Technische Universität Darmstadt. pp. 105-110

Weskamp, Nils; Hüllermeier, Eyke

and Klebe, Gerhard (2009): Merging chemical and biological space: Structural mapping of enzyme binding pocket space. In: Proteins: Structure, Function, and Bioinformatics, Vol. 76, No. 2: pp. 317-330

2008

Cheng, Weiwei and Hüllermeier, Eyke

(2008): Learning Similarity Functions from Qualitative Feedback. In: Althoff, Klaus-Dieter; Bergmann, Ralph; Minor, Mirjam and Hanft, Alexandre (eds.) : Advances in Case-Based Reasoning. 9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. Proceedings. Lecture Notes in Computer Science, Vol. 5239. Berlin, Heidelberg: Springer. pp. 120-134

Hüllermeier, Eyke

(2008): Fuzzy Methods for Data Mining and Machine Learning: State of the Art and Prospects. In: Bustince, Humberto; Herrera, Francisco and Montero, Javier (eds.) : Fuzzy Sets and Their Extensions: Representation, Aggregation and Models. Studies in Fuzziness and Soft Computing, Vol. 220. Berlin, Heidelberg: Springer. pp. 357-375

Hüllermeier, Eyke

(2008): Granular Computing in Machine Learning and Data Mining. In: Pedrycz, Witold; Skowron, Andrzej and Kreinovich, Vladik (eds.) : Handbook of Granular Computing. pp. 889-906

Hüllermeier, Eyke

(2008): Why Fuzzy Set Theory is Useful in Data Mining. In: Poncelet, Pascal; Masseglia, Florent and Teisseire, Maguelonne (eds.) : Successes and New Directions in Data Mining. Hershey ; New York: Information Science Reference. pp. 1-16

Hüllermeier, Eyke

and Fürnkranz, Johannes (2008): Learning Preference Models from Data: On the Problem of Label Ranking and Its Variants. In: Della Riccia, Giacomo; Dubois, Didier; Kruse, Rudolf and Lenz, Hans-Joachim (eds.) : Preferences and Similarities. CISM International Centre for Mechanical Sciences, Vol. 504. Vienna: Springer. pp. 283-304

Hüllermeier, Eyke

; Vladimirskiy, Ilya; Prados Suárez, Belén and Stauch, Eva (2008): Supporting Case-Based Retrieval by Similarity Skylines: Basic Concepts and Extensions. In: Althoff, Klaus-Dieter; Bergmann, Ralph; Minor, Mirjam and Hanft, Alexandre (eds.) : Advances in Case-Based Reasoning. Lecture Notes in Computer Science. 9th European Conference, ECCBR 2008, Trier, Germany, September 1-4, 2008. Proceedings, Vol. 5239. Berlin, Heidelberg: Springer. pp. 240-254

Vanderlooy, Stijn and Hüllermeier, Eyke

(2008): A Critical Analysis of Variants of the AUC. In: Daelemans, Walter; Goethals, Bart and Morik, Katharina (eds.) : Machine Learning and Knowledge Discovery in Databases. European Conference, ECML PKDD 2008, Antwerp, Belgium, September 15-19, 2008, Proceedings, Part I. Lecture Notes in Computer Science, Vol. 5211. Berlin, Heidelberg: Springer. p. 13

2007

Beringer, Jurgen and Hüllermeier, Eyke

(2007): Adaptive Optimization of the Number of Clusters in Fuzzy Clustering. 2007 IEEE International Fuzzy Systems Conference, London, UK, 23-26 July 2007. In: IEEE International Fuzzy Systems Conference, 2007: FUZZ-IEEE 2007 ; 23 - 26 July 2007, London, United Kingdom. Piscataway, NJ: IEEE. pp. 1-6

Beringer, Jürgen and Hüllermeier, Eyke

(2007): Fuzzy Clustering of Parallel Data Streams. In: Oliveira, Jose Valente de and Pedrycz, Witold (eds.) : Advances in Fuzzy Clustering and its Applications. Chichester: Wiley. pp. 333-352

Beringer, Jürgen and Hüllermeier, Eyke

(2007): An Efficient Algorithm for Instance-Based Learning on Data Streams. In: Perner, Petra (ed.) : Advances in Data Mining. Theoretical Aspects and Applications. 7th Industrial Conference, ICDM 2007, Leipzig, Germany, July 14-18, 2007. Proceedings. Lecture Notes in Computer Science, Vol. 4597. Berlin, Heidelberg: Springer. pp. 34-48

Brinker, Klaus and Hüllermeier, Eyke

(2007): Label Ranking in Case-Based Reasoning. In: Weber, Rosina O. and Richter, Michael M. (eds.) : Case-Based Reasoning Research and Development. 7th International Conference on Case-Based Reasoning, ICCBR 2007 Belfast, Northern Ireland, UK, August 13-16, 2007 Proceedings. Lecture Notes in Computer Science, Vol. 4626. Berlin, Heidelberg: Springer. pp. 77-91

Hüllermeier, Eyke

and Fürnkranz, Johannes (2007): On Minimizing the Position Error in Label Ranking. In: Kok, Joost N.; Koronacki, Jacek; Lopez de Mantaras, Raomon; Matwin, Stan; Mladenič, Dunja and Skowron, Andrzej (eds.) : Machine Learning: ECML 2007. 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings. Lecture Notes in Computer Science, Vol. 4701. Berlin, Heidelberg: Springer. pp. 583-590

Hüllermeier, Eyke

; Weskamp, Nils; Klebe, Gerhard and Kuhn, Daniel (2007): Graph Alignment: Fuzzy Pattern Mining for the Structural Analysis of Protein Active Sites. 2007 IEEE International Fuzzy Systems Conference, London, UK, 23-26 July 2007. In: 2007 IEEE International Fuzzy Systems Conference. Piscataway, NJ: IEEE. pp. 1-6

Sulzmann, Jan-Nikolas; Fürnkranz, Johannes and Hüllermeier, Eyke

(2007): On Pairwise Naive Bayes Classifiers. In: Kok, Joost N.; Koronacki, Jacek; Lopez de Mantaras, Raomon; Matwin, Stan; Mladenič, Dunja and Skowron, Andrzej (eds.) : Machine Learning: ECML 2007. 18th European Conference on Machine Learning, Warsaw, Poland, September 17-21, 2007. Proceedings. Lecture Notes in Computer Science, Vol. 4701. Berlin, Heidelberg: Springer. pp. 371-381

2006

Bade, Korinna; Hüllermeier, Eyke

and Nurnberger, Andreas (2006): Hierarchical Classification by Expected Utility Maximization. 6th IEEE International Conference on Data Mining, Hong Kong, China, 18-22 December, 2006. In: Clifton, Christopher W. (ed.) : Sixth IEEE International Conference on Data Mining : ICDM 2006 ; 18-22 December 2006, Hong Kong. Los Alamitos, Calif.: IEEE Computer Society. pp. 43-52

Bodenhofer, Ulrich; Hüllermeier, Eyke

; Klawonn, Frank and Kruse, Rudolf (2006): Special Issue on Soft Computing for Information Mining. Editorial. In: Soft Computing, Vol. 11, No. 5: pp. 397-399

Brinker, Klaus and Hüllermeier, Eyke

(2006): Case-Based Label Ranking. In: Fürnkranz, Johannes; Scheffer, Tobias and Spiliopoulou, Myra (eds.) : Machine Learning: ECML 2006. 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings. Lecture Notes in Computer Science, Vol. 4212. Berlin, Heidelberg: Springer. pp. 566-573

Dubois, Didier; Hüllermeier, Eyke

and Prade, Henri (2006): Fuzzy methods for case-based recommendation and decision support. In: Journal of Intelligent Information Systems, Vol. 27, No. 2: pp. 95-115

Hüllermeier, Eyke

(2006): The Choquet-Integral as an Aggregation Operator in Case-Based Learning. In: Reusch, Bernd (ed.) : Computational Intelligence, Theory and Applications. International Conference 9th Fuzzy Days in Dortmund, Germany, Sept. 18–20, 2006 Proceedings. Berlin, Heidelberg: Springer. pp. 615-627

Karimi, Iman; Hüllermeier, Eyke

and Meskouris, Konstantin (2006): A fuzzy-probabilistic earthquake risk assessment system. In: Soft Computing, Vol. 11, No. 3: pp. 229-238

2005

Dubois, Didier and Hüllermeier, Eyke

(July 2005): A Notion of Comparative Probabilistic Entropy Based on the Possibilistic Specificity Ordering. In: Symbolic and Quantitative Approaches to Reasoning with Uncertainty. 8th European Conference, ECSQARU 2005, Barcelona, Spain, July 6-8, 2005. Proceedings. Lecture Notes in Computer Science, Vol. 3571. Berlin, Heidelberg: Springer. pp. 848-859

Hüllermeier, Eyke

(2005): Fuzzy Methods in Knowledge Discovery. In: Reusch, Bernd (ed.) : Computational Intelligence, Theory and Applications. International Conference 8th Fuzzy Days in Dortmund, Germany, Sept. 29–Oct. 01, 2004 Proceedings. Advances in Soft Computing, Vol. 33. Berlin, Heidelberg: Springer. p. 483

Hüllermeier, Eyke

and Beringer, Jürgen (September 2005): Learning from Ambiguously Labeled Examples. In: Famili, A. Fazel; Kok, Joost N.; Peña, José M.; Siebes, Arno and Feelders, Ad (eds.) : Advances in Intelligent Data Analysis VI. Lecture Notes in Computer Science. 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005. Proceedings, Vol. 3646. Berlin, Heidelberg: Springer. pp. 168-179

Hüllermeier, Eyke

and Fürnkranz, Johannes (September 2005): Learning Label Preferences: Ranking Error Versus Position Error. In: Fazel Famili, A.; Kok, Joost N.; Peña, José M.; Siebes, Arno and Feelders, Ad (eds.) : Advances in Intelligent Data Analysis VI. 6th International Symposium on Intelligent Data Analysis, IDA 2005, Madrid, Spain, September 8-10, 2005. Proceedings. Lecture Notes in Computer Science, Vol. 3646. Berlin, Heidelberg: Springer. pp. 180-191

2004

Hüllermeier, Eyke

and Fürnkranz, J. (2004): Ranking by pairwise comparison a note on risk minimization. 2004 IEEE International Conference on Fuzzy Systems, Budapest, Hungary, 25-29 July, 2004. In: 2004 IEEE International Conference on Fuzzy Systems : proceedings : Budapest, Hungary, 25-29 July, 2004. Vol. 1. Piscataway, N.J: IEEE. pp. 97-102

2003

Dubois, Didier; Hüllermeier, Eyke

and Prade, Henri (2003): A Note on Quality Measures for Fuzzy Association Rules. In: Bilgiç, Taner; De Baets, Bernard and Okyay Kaynak (eds.) : Fuzzy Sets and Systems — IFSA 2003. 10th International Fuzzy Systems Association World Congress Istanbul, Turkey, June 30 – July 2, 2003 Proceedings. Lecture Notes in Computer Science, Vol. 2715. Berlin, Heidelberg: Springer. pp. 346-353

Dubois, Didier; Hüllermeier, Eyke

and Prade, Henri (2003): Flexible Control of Case-Based Prediction in the Framework of Possibility Theory. In: Blanzieri, Enrico and Portinale, Luigi (eds.) : Advances in Case-Based Reasoning. 5th European Workshop, EWCBR 2000 Trento, Italy, September 6–9, 2000 Proceedings. Lecture Notes in Computer Science, Vol. 1898. Berlin, Heidelberg: Springer. pp. 61-73

Dubois, Didier; Hüllermeier, Eyke

and Prade, Henri (2003): Possibilistic case-based decisions. In: Lesage, Cédric and Cottrell, Marie (eds.) : Connectionist Approaches in Economics and Management Sciences. Advances in Computational Management Science, Vol. 6. Boston, MA: Springer. pp. 31-48

Fürnkranz, Johannes and Hüllermeier, Eyke

(September 2003): Pairwise Preference Learning and Ranking. In: Lavrač, Nada; Gamberger, Dragan; Blockeel, Hendrik and Todorovski, Ljupčo (eds.) : Machine Learning: ECML 2003. 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003. Proceedings. Lecture Notes in Computer Science, Vol. 2837. Berlin, Heidelberg: Springer. pp. 145-156

Hüllermeier, Eyke

(August 2003): Regularized Learning with Flexible Constraints. In: Berthold, Michael R.; Lenz, Hans-Joachim; Bradley, Elizabeth; Kruse, Rudolf and Borgelt, Christian (eds.) : Advances in Intelligent Data Analysis V. 5th International Symposium on Intelligent Data Analysis, IDA 2003, Berlin, Germany, August 28-30, 2003. Proceedings. Lecture Notes in Computer Science, Vol. 2810. Berlin, Heidelberg: Springer. pp. 13-24

Hüllermeier, Eyke

(2003): Inducing Fuzzy Concepts through Extended Version Space Learning. In: Bilgiç, Taner; De Baets, Bernard and Kaynak, Okyay (eds.) : Fuzzy Sets and Systems — IFSA 2003. 10th International Fuzzy Systems Association World Congress Istanbul, Turkey, June 30 – July 2, 2003 Proceedings. Lecture Notes in Computer Science, Vol. 2715. Berlin, Heidelberg: Springer. pp. 677-684

Hüllermeier, Eyke

(2003): A Method for Predicting Solutions in Case-Based Problem Solving. In: Blanzieri, Enrico and Portinale, Luigi (eds.) : Advances in Case-Based Reasoning. 5th European Workshop, EWCBR 2000 Trento, Italy, September 6–9, 2000 Proceedings. Lecture Notes in Computer Science, Vol. 1898. Berlin, Heidelberg: Springer. pp. 124-135

Hüllermeier, Eyke

(2003): Sequential Decision Making in Heuristic Search. In: Della Riccia, Giacomo; Dubois, Didier; Kruse, Rudolf and Lenz, Hans-J. (eds.) : Planning Based on Decision Theory. International Centre for Mechanical Sciences, Vol. 472. Vienna: Springer. pp. 27-41

Hüllermeier, Eyke

(September 2003): Instance-Based Learning of Credible Label Sets. In: Günter, Andreas; Kruse, Rudolf and Neumann, Bernd (eds.) : KI 2003: Advances in Artificial Intelligence. 26th Annual German Conference on AI, KI 2003, Hamburg, Germany, September 15-18, 2003. Proceedings. Lecture Notes in Computer Science, Vol. 2821. Berlin, Heidelberg: Springer. pp. 450-464

2002

Hüllermeier, Eyke

(September 2002): Experience-Based Decision Making and Learning from Examples. In: Operations Research Proceedings 2001. Selected Papers of the International Conference on Operations Research (OR 2001) Duisburg, September 3–5, 2001. Berlin, Heidelberg: Springer. pp. 363-370

Hüllermeier, Eyke

(May 2002): Exploiting similarity and experience in decision making. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems: May 12 - 17, 2002, Hilton Hawaiian Village Hotel, Honolulu, Hawaii. Vol. 1. Piscataway, NJ: IEEE. pp. 729-734

Hüllermeier, Eyke

(2002): Association Rules for Expressing Gradual Dependencies. In: Elomaa, Tapio; Mannila, Heikki and Toivonen, Hannu (eds.) : Principles of Data Mining and Knowledge Discovery. 6th European Conference, PKDD 2002 Helsinki, Finland, August 19–23, 2002 Proceedings. Lecture Notes in Computer Science, Vol. 2431. Berlin, Heidelberg: Springer. pp. 200-211

Hüllermeier, Eyke

(2002): Possibilistic Induction in Decision-Tree Learning. In: Elomaa, Tapio; Mannila, Heikki and Toivonen, Hannu (eds.) : Machine Learning: ECML 2002.13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 Proceedings. Lecture Notes in Computer Science, Vol. 2430. Cham: Springer. pp. 173-184

Hüllermeier, Eyke

; Dubois, Didier and Prade, Henri (2002): Knowledge-Based Extrapolation of Cases: A Possibilistic Approach. In: Bouchon-Meunier, Bernadette; Gutiérrez-Ríos, Julio; Magdalena, Luis and Yager, Ronald R. (eds.) : Technologies for Constructing Intelligent Systems 1. Studies in Fuzziness and Soft Computing, Vol. 89. Heidelberg: Physica Verlag. pp. 377-390

de Calmes, Martine; Dubois, Didier; Hüllermeier, Eyke

; Prade, Henri and Sedes, Florence (2002): Case-based querying and prediction: a fuzzy set approach. 2002 IEEE World Congress on Computational Intelligence, Honolulu, HI, USA, 12-17 May 2002. In: 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings. Volume 2. Vol. 1. Piscataway, NJ: IEEE. pp. 735-739

2001

Dubois, Didier; Hüllermeier, Eyke

and Prade, Henri (July 2001): Toward the representation of implication-based fuzzy rules in terms of crisp rules. 20th NAFIPS International Conference, Vancouver, BC, Canada, 25-28 July 2001. In: Smith, Michael H. (ed.) : Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569). Vol. 3. IEEE Operations Center: Piscataway, NJ. pp. 1592-1597

Hüllermeier, Eyke

(2001): Implication-Based Fuzzy Association Rules. In: Principles of Data Mining and Knowledge Discovery. 5th European Conference, PKDD 2001, Freiburg, Germany, September 3–5, 2001 Proceedings. Lecture Notes in Computer Science, Vol. 2168. Berlin, Heidelberg: Springer. pp. 241-252

Hüllermeier, Eyke

(October 2001): Fuzzy Association Rules: Semantic Issues and Quality Measures. In: Reusch, Bernd (ed.) : Computational Intelligence. Theory and Applications. International Conference, 7th Fuzzy Days Dortmund, Germany, October 1–3, 2001 Proceedings. Lecture Notes in Computer Science, Vol. 2206. Berlin, Heidelberg: Springer. pp. 380-391

1999

Hüllermeier, Eyke

(1999): Exploiting Similarity for Supporting Data Analysis and Problem Solving. In: Hand, David J.; Kok, Joost N. and Berthold, Michael R. (eds.) : Advances in Intelligent Data Analysis. Third International Symposium, IDA-99 Amsterdam, The Netherlands, August 9–11, 1999 Proceedings. Lecture Notes in Computer Science, Vol. 1642. Berlin, Heidelberg: Springer. pp. 257-268

Hüllermeier, Eyke

(May 1999): A Possibilistic Formalization of Case-Based Reasoning and Decision Making. In: Reusch, Bernd (ed.) : Computational Intelligence. Theory and Applications International Conference, 6th Fuzzy Days Dortmund, Germany, May 25–28 1999 Proceedings. Lecture Notes in Computer Science, Vol. 1625. Berlin, Heidelberg: Springer. pp. 411-420

This list was generated on Mon Dec 11 17:09:51 2023 CET.