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Conformal Rule-Based Multi-label Classification
Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencia · 2020-07-16 · via cs.LG updates on arXiv.org

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.

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