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Machine Learning and Knowledge Discovery in Databases

Volume 7523 of the series Lecture Notes in Computer Science pp 665-680

Learning and Inference in Probabilistic Classifier Chains with Beam Search

  • Abhishek KumarAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, UC San Diego
  • , Shankar VembuAffiliated withCarnegie Mellon UniversityDonnelly Centre for Cellular and Biomolecular Research, University of Toronto
  • , Aditya Krishna MenonAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, UC San Diego
  • , Charles ElkanAffiliated withCarnegie Mellon UniversityDepartment of Computer Science, UC San Diego

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Abstract

Multilabel learning is an extension of binary classification that is both challenging and practically important. Recently, a method for multilabel learning called probabilistic classifier chains (PCCs) was proposed with numerous appealing properties, such as conceptual simplicity, flexibility, and theoretical justification. However, PCCs suffer from the computational issue of having inference that is exponential in the number of tags, and the practical issue of being sensitive to the suitable ordering of the tags while training. In this paper, we show how the classical technique of beam search may be used to solve both these problems. Specifically, we show how to use beam search to perform tractable test time inference, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of multilabel datasets show that these proposed changes dramatically extend the practical viability of PCCs.