Eyke Hüllermeier, Johannes Fürnkranz, Eneldo Loza Mencía, Vu-Linh Nguyen, Michael Rapp,
"Rule-Based Multi-label Classification: Challenges and Opportunities"
, in Victor Gutierrez-Basulto, Tomas Kliegr, Ahmet Soylu, Martin Giese, and Dumitru Roman: Proceedings of the 4th International Joint Conference on Rules and Reasoning (RuleML+RR), Serie Lecture Notes in Computer Science, Vol. 12173, Springer, Oslo, Norway, Seite(n) 3-19, 2020
Rule-Based Multi-label Classification: Challenges and Opportunities
Sprache des Titels:
Proceedings of the 4th International Joint Conference on Rules and Reasoning (RuleML+RR)
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.