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Classifier Ensembles for Changing Environments

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Multiple Classifier Systems (MCS 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3077))

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Abstract

We consider strategies for building classifier ensembles for non-stationary environments where the classification task changes during the operation of the ensemble. Individual classifier models capable of online learning are reviewed. The concept of ”forgetting” is discussed. Online ensembles and strategies suitable for changing environments are summarized.

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Author information

Authors and Affiliations

  1. School of Informatics, University of Wales, Bangor, Bangor, Gwynedd, LL57 1UT, United Kingdom

    Ludmila I. Kuncheva

Authors
  1. Ludmila I. Kuncheva
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Editor information

Editors and Affiliations

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

    Fabio Roli

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

    Josef Kittler

  3. Centre for Vision, Speech and Signal Proc (CVSSP), University of Surrey, GU2 7XH, Guildford, Surrey, United Kingdom

    Terry Windeatt

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© 2004 Springer-Verlag Berlin Heidelberg

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Kuncheva, L.I. (2004). Classifier Ensembles for Changing Environments. In: Roli, F., Kittler, J., Windeatt, T. (eds) Multiple Classifier Systems. MCS 2004. Lecture Notes in Computer Science, vol 3077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25966-4_1

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  • DOI: https://doi.org/10.1007/978-3-540-25966-4_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22144-9

  • Online ISBN: 978-3-540-25966-4

  • eBook Packages: Springer Book Archive

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