Robust Feature Selection Using Ensemble Feature Selection Techniques

  • Yvan Saeys
  • Thomas Abeel
  • Yves Van de Peer
Conference paper

DOI: 10.1007/978-3-540-87481-2_21

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5212)
Cite this paper as:
Saeys Y., Abeel T., Van de Peer Y. (2008) Robust Feature Selection Using Ensemble Feature Selection Techniques. In: Daelemans W., Goethals B., Morik K. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2008. Lecture Notes in Computer Science, vol 5212. Springer, Berlin, Heidelberg

Abstract

Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, where multiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains with small sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemble feature selection techniques on classification performance, giving rise to a new model selection strategy.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Yvan Saeys
    • 1
    • 2
  • Thomas Abeel
    • 1
    • 2
  • Yves Van de Peer
    • 1
    • 2
  1. 1.Department of Plant Systems BiologyVIBGentBelgium
  2. 2.Department of Molecular GeneticsGhent UniversityGentBelgium

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