Title
Learning Interpretable Rules for Multi-label Classification.
Abstract
Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.
Year
DOI
Venue
2018
10.1007/978-3-319-98131-4_4
arXiv: Learning
DocType
Volume
Citations 
Journal
abs/1812.00050
1
PageRank 
References 
Authors
0.36
34
4
Name
Order
Citations
PageRank
Eneldo Loza Menc ´ ia148825.84
Johannes Fürnkranz22476222.90
Eyke Hüllermeier33423213.52
Michael Rapp423.08