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Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

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2 Author(s)
Feng Yang ; Div. of Control & Instrum., Nanyang Technol. Univ., Singapore, Singapore ; Mao, K.Z.

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.

Published in:

Computational Biology and Bioinformatics, IEEE/ACM Transactions on  (Volume:8 ,  Issue: 4 )

Date of Publication:

July-Aug. 2011

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