%Aigaion2 BibTeX export from Knowledge Engineering Publications %Friday 17 December 2021 11:56:21 PM @INPROCEEDINGS{huellermeier20MLRLchallenges, author = {H{\"{u}}llermeier, Eyke and F{\"{u}}rnkranz, Johannes and Loza Menc{\'{\i}}a, Eneldo and Nguyen, Vu-Linh and Rapp, Michael}, editor = {Guti{\'{e}}rrez-Basulto, V{\'{\i}}ctor and Kliegr, Tom{\'{a}}s and Soylu, Ahmet and Giese, Martin and Roman, Dumitru}, keywords = {multilabel classification, Rule Learning}, month = aug, title = {Rule-Based Multi-label Classification: Challenges and Opportunities}, booktitle = {Rules and Reasoning}, series = {Lecture Notes in Computer Science}, volume = {12173}, year = {2020}, pages = {3--19}, publisher = {Springer International Publishing}, isbn = {978-3-030-57977-7}, url = {https://link.springer.com/chapter/10.1007/978-3-030-57977-7_1}, doi = {https://doi.org/10.1007/978-3-030-57977-7_1}, abstract = {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.} }