Rule-Based Multi-label Classification: Challenges and Opportunities

Hüllermeier, Eyke ; Fürnkranz, Johannes; Loza Mencia, Eneldo; Nguyen, Vu-Linh and Rapp, Michael (August 2020): Rule-Based Multi-label Classification: Challenges and Opportunities. 4th International Joint Conference on Rules and Reasoning, Virtual, 29 June - 1 July 2020. In: Rules and Reasoning, Vol. 12173 Cham: Springer. pp. 3-19

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.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISSN: 0302-9743
• Place of Publication: Cham
• Language: English