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Learning Interpretable Rules for Multi-Label Classification

  • Eneldo Loza MencíaEmail author
  • Johannes Fürnkranz
  • Eyke Hüllermeier
  • Michael Rapp
Chapter
Part of the The Springer Series on Challenges in Machine Learning book series (SSCML)

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.

Keywords

Multi-label classification Label-dependencies Rule learning Separate-and-conquer 

Notes

Acknowledgements

We would like to thank Frederik Janssen for his contributions to this work. Computations for this research were conducted on the Lichtenberg high performance computer of the TU Darmstadt.

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Authors and Affiliations

  • Eneldo Loza Mencía
    • 1
    Email author
  • Johannes Fürnkranz
    • 1
  • Eyke Hüllermeier
    • 2
  • Michael Rapp
    • 1
  1. 1.Knowledge Engineering GroupTechnische Universität DarmstadtDarmstadtGermany
  2. 2.Intelligent SystemsUniversität PaderbornPaderbornGermany

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