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Multi-category Classification by Soft-Max Combination of Binary Classifiers

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Multiple Classifier Systems (MCS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2709))

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Abstract

In this paper, we propose a multi-category classification method that combines binary classifiers through soft-max function. Posteriori probabilities are also obtained. Both, one-versus-all and one-versus- one classifiers can be used in the combination. Empirical comparison shows that the proposed method is competitive with other implementations of one-versus-all and one-versus-one methods in terms of both classification accuracy and posteriori probability estimate.

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Author information

Authors and Affiliations

  1. Control Division, Department of Mechanical Engineering, National University of Singapore, Singapore, 119260

    Kaibo Duan, S. Sathiya Keerthi, Wei Chu & Aun Neow Poo

  2. Department of Computer Science and Automation, Indian Institute of Science, Bangalore, 560 012

    Shirish Krishnaj Shevade

Authors
  1. Kaibo Duan
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  2. S. Sathiya Keerthi
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  3. Wei Chu
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  4. Shirish Krishnaj Shevade
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  5. Aun Neow Poo
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Editor information

Editors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, Surrey, GU2 7XH, UK

    Terry Windeatt

  2. Dept. of Electrical and Electronic Engineering, University of Cagliari, Piazza D’Ami, 09123, Cagliari, Italy

    Fabio Roli

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© 2003 Springer-Verlag Berlin Heidelberg

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Duan, K., Keerthi, S.S., Chu, W., Shevade, S.K., Poo, A.N. (2003). Multi-category Classification by Soft-Max Combination of Binary Classifiers. In: Windeatt, T., Roli, F. (eds) Multiple Classifier Systems. MCS 2003. Lecture Notes in Computer Science, vol 2709. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44938-8_13

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  • DOI: https://doi.org/10.1007/3-540-44938-8_13

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40369-2

  • Online ISBN: 978-3-540-44938-6

  • eBook Packages: Springer Book Archive

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