  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.
