Robust Feature Selection using Ensemble Feature Selection Techniques
Robustness or stability of feature selection techniques is a topic of recent interest, and is an important issue when selected feature subsets are subsequentlyanalysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the use of ensemble feature selection techniques, wheremultiple feature selection methods are combined to yield more robust results. We show that these techniques show great promise for high-dimensional domains withsmall sample sizes, and provide more robust feature subsets than a single feature selection technique. In addition, we also investigate the effect of ensemblefeature selection techniques on classification performance, giving rise to a new model selection strategy.
Saeys, Y., Abeel, T., Van de Peer, Y. (2008) Robust Feature Selection using Ensemble Feature Selection Techniques. Proceedings of ECML/PKDD 5212:313-25.
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Bioinformatics & Evolutionary Genomics
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