Selective Ensemble of Classifier Chains

  • Nan Li
  • Zhi-Hua Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7872)

Abstract

In multi-label learning, the relationship among labels is well accepted to be important, and various methods have been proposed to exploit label relationships. Amongst them, ensemble of classifier chains (ECC) which builds multiple chaining classifiers by random label orders has drawn much attention. However, the ensembles generated by ECC are often unnecessarily large, leading to extra high computational and storage cost. To tackle this issue, in this paper, we propose selective ensemble of classifier chains (SECC) which tries to select a subset of classifier chains to composite the ensemble whilst keeping or improving the performance. More precisely, we focus on the performance measure F1-score, and formulate this problem as a convex optimization problem which can be efficiently solved by the stochastic gradient descend method. Experiments show that, compared with ECC, SECC is able to obtain much smaller ensembles while achieving better or at least comparable performance.

Keywords

multi-label classifier chains selective ensemble 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nan Li
    • 1
    • 2
  • Zhi-Hua Zhou
    • 1
  1. 1.National Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.School of Mathematical SciencesSoochow UniversitySuzhouChina

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