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International Workshop on Multiple Classifier Systems

MCS 2015: Multiple Classifier Systems pp 76–88Cite as

Multi-label Selective Ensemble

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9132))

Abstract

Multi-label selective ensemble deals with the problem of reducing the size of multi-label ensembles whilst keeping or improving the performance. In practice, it is of important value, since the generated ensembles are usually unnecessarily large, which leads to extra high computational and storage cost. However, it is more challenging than traditional selective ensemble, because real-world applications often employ different performance measures to evaluate the quality of multi-label predictions, depending on user requirements. In this paper, we propose the MUSE approach to tackle this problem. Specifically, by directly considering the concerned performance measure, we develop a convex optimization formulation and provide an efficient stochastic optimization solution for a large variety of multi-label performance measures. Experiments show that MUSE is able to obtain smaller multi-label ensembles, whilst achieving better or at least comparable performance in terms of the concerned performance measure.

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Acknowledgements

We want to thank anonymous reviewers for helpful comments. This research was supported by the National Science Foundation of China (61273301).

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, 210023, China

    Nan Li, Yuan Jiang & Zhi-Hua Zhou

Authors
  1. Nan Li
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  2. Yuan Jiang
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  3. Zhi-Hua Zhou
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Corresponding author

Correspondence to Nan Li .

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Editors and Affiliations

  1. Ulm University, Ulm, Germany

    Friedhelm Schwenker

  2. University of Cagliari, Cagliari, Italy

    Fabio Roli

  3. University of Surrey, Guildford, United Kingdom

    Josef Kittler

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Li, N., Jiang, Y., Zhou, ZH. (2015). Multi-label Selective Ensemble. In: Schwenker, F., Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2015. Lecture Notes in Computer Science(), vol 9132. Springer, Cham. https://doi.org/10.1007/978-3-319-20248-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-20248-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20247-1

  • Online ISBN: 978-3-319-20248-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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