Random k-Labelsets: An Ensemble Method for Multilabel Classification

  • Grigorios Tsoumakas
  • Ioannis Vlahavas
Conference paper

DOI: 10.1007/978-3-540-74958-5_38

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4701)
Cite this paper as:
Tsoumakas G., Vlahavas I. (2007) Random k-Labelsets: An Ensemble Method for Multilabel Classification. In: Kok J.N., Koronacki J., Mantaras R.L.., Matwin S., Mladenič D., Skowron A. (eds) Machine Learning: ECML 2007. ECML 2007. Lecture Notes in Computer Science, vol 4701. Springer, Berlin, Heidelberg

Abstract

This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Grigorios Tsoumakas
    • 1
  • Ioannis Vlahavas
    • 1
  1. 1.Department of Informatics, Aristotle University of Thessaloniki, 54124 ThessalonikiGreece

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