Robust Feature Selection for Microarray Data Based on Multicriterion Fusion

Authors:
Feng Yang;K. Z. Mao
Affiliations:
Nanyang Technological University, Singapore;Nanyang Technological University, Singapore
Venue:
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Year:
2011

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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.