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Improving Product by Moderating k-NN Classifiers

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Multiple Classifier Systems (MCS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2096))

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Abstract

The veto effect caused by contradicting experts outputting zero probability estimates leads to fusion strategies performing sub optimally. This can be resolved using Moderation. The Moderation formula is derived for the k-NN classifier using a bayesian prior. The merits of moderation are examined on real data sets.

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Author information

Authors and Affiliations

  1. Centre for Vision, Speech and Signal Processing, School of Electronics, Computing and Mathematics, University of Surrey, Guildford, GU2 5XH, UK

    F. M. Alkoot & J. Kittler

Authors
  1. F. M. Alkoot
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  2. J. Kittler
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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

    Josef Kittler

  2. Department of Electrical and Electronic Engineering, University of Cagliari, Piazza d’Armi, 09123, Cagliari, Italy

    Fabio Roli

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© 2001 Springer-Verlag Berlin Heidelberg

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Alkoot, F.M., Kittler, J. (2001). Improving Product by Moderating k-NN Classifiers. In: Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2001. Lecture Notes in Computer Science, vol 2096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48219-9_43

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  • DOI: https://doi.org/10.1007/3-540-48219-9_43

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42284-6

  • Online ISBN: 978-3-540-48219-2

  • eBook Packages: Springer Book Archive

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