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Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
July/August 2011 (vol. 8 no. 4)
pp. 1080-1092
Feng Yang, Nanyang Technological University, Singapore
K.Z. Mao, Nanyang Technological University, Singapore
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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Index Terms:
Feature selection, multicriterion fusion, recursive feature elimination, robustness, classification.
Citation:
Feng Yang, K.Z. Mao, "Robust Feature Selection for Microarray Data Based on Multicriterion Fusion," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 4, pp. 1080-1092, July-Aug. 2011, doi:10.1109/TCBB.2010.103
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