Robust Feature Selection Using Ensemble Feature Selection Techniques

Authors:
Yvan Saeys;Thomas Abeel;Yves Peer
Affiliations:
Department of Plant Systems Biology, VIB, Gent, Belgium 9052 and Department of Molecular Genetics, Ghent University, Gent, Belgium;Department of Plant Systems Biology, VIB, Gent, Belgium 9052 and Department of Molecular Genetics, Ghent University, Gent, Belgium;Department of Plant Systems Biology, VIB, Gent, Belgium 9052 and Department of Molecular Genetics, Ghent University, Gent, Belgium
Venue:
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Year:
2008

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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.