Rule-Based Multi-label Classification: Challenges and Opportunities

Hüllermeier, Eyke ; Fürnkranz, Johannes; Loza Mencia, Eneldo; Nguyen, Vu-Linh und 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, Bd. 12173 Cham: Springer. S. 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.

• Dokumententyp: Konferenzbeitrag (Paper)
• Publikationsform: Publisher's Version
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme
• ISSN: 0302-9743
• Ort: Cham
• Sprache: Englisch