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Multiple Classifier Combination Methodologies for Different Output Levels

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Multiple Classifier Systems (MCS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1857))

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Abstract

In the past decade, many researchers have employed various methodologies to combine decisions of multiple classifiers in order to order to improve recognition results. In this article, we will examine the main combination methods that have been developed for different levels of classifier outputs - abstract level, ranked list of classes, and measurements. At the same time, various issues, results, and applications of these methods will also be considered, and these will illustrate the diversity and scope of this research area.

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Authors and Affiliations

  1. Centre for Pattern Recognition and Machine Intelligence, Concordia University, 1455 de Maisonneuve Blvd. West, Montréal, Québec, H3G 1M8, Canada

    Ching Y. Suen & Louisa Lam

  2. Department of Mathematics, Hong Kong Institute of Education, 10 Lo Ping Road, Tai Po, Hong Kong

    Louisa Lam

Authors
  1. Ching Y. Suen
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  2. Louisa Lam
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© 2000 Springer-Verlag Berlin Heidelberg

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Suen, C.Y., Lam, L. (2000). Multiple Classifier Combination Methodologies for Different Output Levels. In: Multiple Classifier Systems. MCS 2000. Lecture Notes in Computer Science, vol 1857. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45014-9_5

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  • DOI: https://doi.org/10.1007/3-540-45014-9_5

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67704-8

  • Online ISBN: 978-3-540-45014-6

  • eBook Packages: Springer Book Archive

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