ORCID: https://orcid.org/0000-0002-9944-4108; Fürnkranz, Johannes und 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
Cham: Springer. pp. 290-296
[PDF, 350kB]

Abstract
We advocate the use of conformal prediction (CP) to enhance rule-based multi-label classification (MLC). In particular, we highlight the mutual benefit of CP and rule learning: Rules have the ability to provide natural (non-)conformity scores, which are required by CP, while CP suggests a way to calibrate the assessment of candidate rules, thereby supporting better predictions and more elaborate decision making. We illustrate the potential usefulness of calibrated conformity scores in a case study on lazy multi-label rule learning.
Item Type: | Conference or Workshop Item (Paper) |
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Form of publication: | Publisher's Version |
Faculties: | Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning |
Subjects: | 000 Computer science, information and general works > 000 Computer science, knowledge, and systems |
URN: | urn:nbn:de:bvb:19-epub-92519-9 |
ISSN: | 0302-9743 |
Place of Publication: | Cham |
Language: | English |
Item ID: | 92519 |
Date Deposited: | 09. Sep 2022 11:21 |
Last Modified: | 04. Dec 2024 10:36 |