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Multiple Classification Systems in the Context of Feature Extraction and Selection

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Book cover Multiple Classifier Systems (MCS 2002)

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Abstract

Parallels between Feature Extraction / Selection and Multiple Classification Systems methodologies are considered. Both approaches allow the designer to introduce prior information about the pattern recognition task to be solved. However, both are heavily affected by computational difficulties and by the problem of small sample size / classifier complexity. Neither approach is capable of selecting a unique data analysis algorithm.

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Author information

Authors and Affiliations

  1. Vilnius Gediminas Technical University, Saulėtekio 11, Vilnius, Lithuania

    Šarūnas Raudys

Authors
  1. Šarūnas Raudys
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Editor information

Editors and Affiliations

  1. Dept. of Electrical and Electronical Engineering, University of Cagliari, Piazza D’Armi, 09123, Cagliari, Italy

    Fabio Roli

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

    Josef Kittler

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© 2002 Springer-Verlag Berlin Heidelberg

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Raudys, Š. (2002). Multiple Classification Systems in the Context of Feature Extraction and Selection. In: Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2002. Lecture Notes in Computer Science, vol 2364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45428-4_3

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  • DOI: https://doi.org/10.1007/3-540-45428-4_3

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43818-2

  • Online ISBN: 978-3-540-45428-1

  • eBook Packages: Springer Book Archive

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