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Feature Selection Method Using Preferences Aggregation

  • Conference paper
Book cover Machine Learning and Data Mining in Pattern Recognition (MLDM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3587))

Abstract

The feature selection allows to choose P features among M (P<M) and thus to reduce the representation space of data. This process is increasingly useful because of the databases size increase. Therefore we propose a method based on preferences aggregation. It is an hybrid method between filter and wrapper approaches.

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Author information

Authors and Affiliations

  1. Laboratoire ERIC, Université Lumière Lyon 2, Bât. L-5, av. Pierre Mendès-France, 69676, Bron Cedex, France

    Gaëlle Legrand & Nicolas Nicoloyannis

Authors
  1. Gaëlle Legrand
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  2. Nicolas Nicoloyannis
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

  2. Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya

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© 2005 Springer-Verlag Berlin Heidelberg

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Legrand, G., Nicoloyannis, N. (2005). Feature Selection Method Using Preferences Aggregation. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_21

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  • DOI: https://doi.org/10.1007/11510888_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26923-6

  • Online ISBN: 978-3-540-31891-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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