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A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy

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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

A method for multiple classifier selection and combination is presented. Classifiers are selected sequentially on-line based on a context specific (data driven) formulation of classifier optimality. A finite subset of a large (or infinite) set of classifiers is used for classification resulting not only in a computational saving, but a boost in classification performance. Experiments were carried out using single class binary classifiers on multi-class classification problems. Classifier outputs are combined using a Bayesian approach and results show a significant improvement in classification accuracy over the AdaBoost. MH method.

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Author information

Authors and Affiliations

  1. University of Leeds, Leeds, UK

    Derek Magee

Authors
  1. Derek Magee
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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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Magee, D. (2003). A Sequential Scheduling Approach to Combining Multiple Object Classifiers Using Cross-Entropy. 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_14

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  • DOI: https://doi.org/10.1007/3-540-44938-8_14

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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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