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Hüllermeier, Eyke ORCID: 0000-0002-9944-4108; 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.
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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.