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Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
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DOI：10.1109/TCBB.2010.103.png)
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Abstract
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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.
Keywords:
Feature selection
multicriterion fusion
recursive feature elimination
robustness
classification
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A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
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