Gene selection using genetic algorithm and support vectors machines

Authors:
Shutao Li;Xixian Wu;Xiaoyan Hu
Affiliations:
Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, People’s Republic of China;Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, People’s Republic of China;Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, People’s Republic of China
Venue:
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on neural networks for pattern recognition and data mining
Year:
2008

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Abstract

In this paper, we present a gene selection method based on genetic algorithm (GA) and support vector machines (SVM) for cancer classification. First, the Wilcoxon rank sum test is used to filter noisy and redundant genes in high dimensional microarray data. Then, the different highly informative genes subsets are selected by GA/SVM using different training sets. The final subset, consisting of highly discriminating genes, is obtained by analyzing the frequency of appearance of each gene in the different gene subsets. The proposed method is tested on three open datasets: leukemia, breast cancer, and colon cancer data. The results show that the proposed method has excellent selection and classification performance, especially for breast cancer data, which can yield 100% classification accuracy using only four genes.