[BibTeX] [RIS]
Rule-Based Multi-label Classification: Challenges and Opportunities
Type of publication: Inproceedings
Citation: huellermeier20MLRLchallenges
Booktitle: Rules and Reasoning
Series: Lecture Notes in Computer Science
Volume: 12173
Year: 2020
Month: August
Pages: 3--19
Publisher: Springer International Publishing
ISBN: 978-3-030-57977-7
URL: https://link.springer.com/chapter/10.1007/978-3-030-57977-7_1
DOI: https://doi.org/10.1007/978-3-030-57977-7_1
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.
Keywords: multilabel classification, Rule Learning
Authors Hüllermeier, Eyke
Fürnkranz, Johannes
Loza Mencía, Eneldo
Nguyen, Vu-Linh
Rapp, Michael
Editors Gutiérrez-Basulto, Víctor
Kliegr, Tomás
Soylu, Ahmet
Giese, Martin
Roman, Dumitru