A hybrid feature selection approach for microarray gene expression data

Authors:
Feng Tan;Xuezheng Fu;Hao Wang;Yanqing Zhang;Anu Bourgeois
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA
Venue:
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part II
Year:
2006

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Abstract

Due to the huge number of genes and comparatively small number of samples from microarray gene expression data, accurate classification of diseases becomes challenging. Feature selection techniques can improve the classification accuracy by removing irrelevant and redundant genes. However, the performance of different feature selection algorithms based on different theoretic arguments varies even when they are applied to the same data set. In this paper, we propose a hybrid approach to combine useful outcomes from different feature selection methods through a genetic algorithm. The experimental results demonstrate that our approach can achieve better classification accuracy with a smaller gene subset than each individual feature selection algorithm does.