Issue No. 04 - July/August (2011 vol. 8)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2010.103
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.
Feature selection, multicriterion fusion, recursive feature elimination, robustness, classification.
F. Yang and K. Mao, "Robust Feature Selection for Microarray Data Based on Multicriterion Fusion," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 1080-1092, 2010.