Conformal Rule-Based Multi-label Classification

Hüllermeier, Eyke ; Fürnkranz, Johannes; Loza Mencia, Eneldo (2020): Conformal Rule-Based Multi-label Classification. KI 2020: Advances in Artificial Intelligence. 43rd German Conference on AI, September 21–25, 2020, Bamberg, Germany.

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)
• Form of publication: Publisher's Version
• Published in: Lecture Notes in Computer Science, Vol. 12325 pp. 290-296
• 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
• ISSN: 0302-9743
• Place of Publication: Cham
• Language: English