Robust feature selection for microarray data based on multicriterion fusion - PubMed (original) (raw)

. 2011 Jul-Aug;8(4):1080-92.

doi: 10.1109/TCBB.2010.103.

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Robust feature selection for microarray data based on multicriterion fusion

Feng Yang et al. IEEE/ACM Trans Comput Biol Bioinform. 2011 Jul-Aug.

Abstract

Feature selection often aims to select a compact feature subset to build a pattern classifier with reduced complexity, so as to achieve improved classification performance. From the perspective of pattern analysis, producing stable or robust solution is also a desired property of a feature selection algorithm. However, the issue of robustness is often overlooked in feature selection. In this study, we analyze the robustness issue existing in feature selection for high-dimensional and small-sized gene-expression data, and propose to improve robustness of feature selection algorithm by using multiple feature selection evaluation criteria. Based on this idea, a multicriterion fusion-based recursive feature elimination (MCF-RFE) algorithm is developed with the goal of improving both classification performance and stability of feature selection results. Experimental studies on five gene-expression data sets show that the MCF-RFE algorithm outperforms the commonly used benchmark feature selection algorithm SVM-RFE.

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