TY - CONF ID - huellermeier20MLRLchallenges T1 - Rule-Based Multi-label Classification: Challenges and Opportunities A1 - Hüllermeier, Eyke A1 - Fürnkranz, Johannes A1 - Loza Mencía, Eneldo A1 - Nguyen, Vu-Linh A1 - Rapp, Michael ED - Gutiérrez-Basulto, Víctor ED - Kliegr, Tomás ED - Soylu, Ahmet ED - Giese, Martin ED - Roman, Dumitru TI - Rules and Reasoning T3 - Lecture Notes in Computer Science Y1 - 2020 VL - 12173 SP - 3 EP - 19 PB - Springer International Publishing SN - 978-3-030-57977-7 UR - https://link.springer.com/chapter/10.1007/978-3-030-57977-7_1 M2 - doi: https://doi.org/10.1007/978-3-030-57977-7_1 KW - multilabel classification KW - Rule Learning N2 - In the context of multi-label classification (MLC), rule-based learning algorithms have a number of appealing properties that are not, at least not as a whole, shared by other approaches. This includes the potential interpretability of rules, their ability to model (local) label dependencies in a flexible way, and the facile customization of a predictor to different loss functions. In this paper, we present a modular framework for rule-based MLC and discuss related challenges and opportunities for multi-label rule learning. ER -