Learning Interpretable Rules for Multi-label Classification

2018-11-30 · Eneldo Loza Mencía, Johannes Fürnkranz, Eyke Hüllermeier, Michael Rapp

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.

📄 PDF Abstract BibTeX arXiv:1812.00050

Code (1)

keelm/SeCo-MLC 공식 구현

Tasks

Classification General Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION

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Multi-Label Classification MUlTI-LABEL-ClASSIFICATION Zero-Shot Learning

On the Trade-off Between Consistency and Coverage in Multi-label Rule Learning Heuristics

2019-08-08 · Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz

Recently, several authors have advocated the use of rule learning algorithms to model multi-label data, as rules are interpretable and can be comprehended, analyzed, or qualitatively evaluated by domain experts. Many rul…

Multi-Label Classification MUlTI-LABEL-ClASSIFICATION