TY - CONF ID - huellermeier20conformal T1 - Conformal Rule-Based Multi-label Classification A1 - Hüllermeier, Eyke A1 - Fürnkranz, Johannes A1 - Loza Mencía, Eneldo ED - Schmid, Ute ED - Klügl, Franziska ED - Wolter, Diedrich TI - KI 2020: Advances in Artificial Intelligence T3 - Lecture Notes in Computer Science Y1 - 2020 VL - 12325 PB - Springer, Cham SN - 978-3-030-58284-5 UR - https://arxiv.org/abs/2007.08145 M2 - doi: 10.1007/978-3-030-58285-2_25 N2 - 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. ER -