A novel hybrid feature selection method for microarray data analysis

Authors:
Chien-Pang Lee;Yungho Leu
Affiliations:
Department of Information Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 106, Taiwan;Department of Information Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Taipei 106, Taiwan
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Recently, many methods have been proposed for microarray data analysis. One of the challenges for microarray applications is to select a proper number of the most relevant genes for data analysis. In this paper, we propose a novel hybrid method for feature selection in microarray data analysis. This method first uses a genetic algorithm with dynamic parameter setting (GADP) to generate a number of subsets of genes and to rank the genes according to their occurrence frequencies in the gene subsets. Then, this method uses the @g^2-test for homogeneity to select a proper number of the top-ranked genes for data analysis. We use the support vector machine (SVM) to verify the efficiency of the selected genes. Six different microarray datasets are used to compare the performance of the GADP method with the existing methods. The experimental results show that the GADP method is better than the existing methods in terms of the number of selected genes and the prediction accuracy.