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@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.}
}