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.
CITATION STYLE
Hüllermeier, E., Fürnkranz, J., Loza Mencia, E., Nguyen, V. L., & Rapp, M. (2020). Rule-Based Multi-label Classification: Challenges and Opportunities. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12173 LNCS, pp. 3–19). Springer. https://doi.org/10.1007/978-3-030-57977-7_1
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