Rule-Based Multi-label Classification - Challenges and Opportunities

Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp

Research output: Contribution to conferencePaperAcademic

1 Citation (Scopus)


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.

Original languageEnglish
Number of pages17
Publication statusPublished - 2020
Externally publishedYes

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  • Multi-label classification
  • Rule learning


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