Towards robust feature selection techniques
Robustness of feature selection techniques is a topic of recent interest, especially in high dimensional domains with small sample sizes, where selected feature
subsets are subsequently analysed by domain experts to gain more insight into the problem modelled. In this work, we investigate the robustness of various feature
selection techniques, and provide a general scheme to improve robustness using ensemble feature selection. We show that ensemble feature selection techniques show
great promise for small sample domains, 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.
Saeys, Y., Abeel, T., Van de Peer, Y. (2008) Towards robust feature selection techniques. Proceedings of Benelearn 45-46. |
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