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Advances in Statistical Feature Selection

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Advances in Pattern Recognition — ICAPR 2001 (ICAPR 2001)

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Abstract

The problem of feature selection in statistical pattern recognition is addressed. After formulating feature selection as a combinatorial optimisation problem, a taxonomy of approaches to feature selection is introduced. The techniques available in the literature can be logically grouped into two main categories depending on the form of density functions involved. Recent advances in the methodology of feature selection are then overviewed in this taxonomical framework. The methods discussed include the latest variants of the Branch & Bound algorithm, enhanced Floating Search techniques and the simultaneous semiparametric pfd modelling and feature space selection method.1

This work was partially supported by EPSRC Grant GR/L61095 and Czech Ministry of Education Grants MŠMT No. VS96063, ME187, CEZ:J18/98:311600001

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Author information

Authors and Affiliations

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

    Josef Kittler

  2. Dept. of Pattern Recognition, Inst. of Information Theory and Automation, Academy of Sciences of the Czech Republic, 182 08, Prague 8, Czech Republic

    Josef Kittler, Pavel Pudil & Petr Somol

Authors
  1. Josef Kittler
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  2. Pavel Pudil
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  3. Petr Somol
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Exeter, EX4 4PT, Exeter, UK

    Sameer Singh

  2. Computational Intelligence Group, Tuiuti University of Parana, Curitiba, Brazil

    Nabeel Murshed

  3. Institute of Computer Aided Automation PRIP-Group 1832, Vienna University of Technology, Favoritenstr. 9/2/4, 1040, Wien, Austria

    Walter Kropatsch

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© 2001 Springer-Verlag Berlin Heidelberg

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Kittler, J., Pudil, P., Somol, P. (2001). Advances in Statistical Feature Selection. In: Singh, S., Murshed, N., Kropatsch, W. (eds) Advances in Pattern Recognition — ICAPR 2001. ICAPR 2001. Lecture Notes in Computer Science, vol 2013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44732-6_44

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  • DOI: https://doi.org/10.1007/3-540-44732-6_44

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41767-5

  • Online ISBN: 978-3-540-44732-0

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