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Issue No. 01 - January-February (2011 vol. 8)
ISSN: 1545-5963
pp: 122-129
Lin-Kai Luo , Xiamen University, Xiamen
Deng-Feng Huang , Xiamen University, Xiamen
Ling-Jun Ye , Xiamen University, Xiamen
Qi-Feng Zhou , Xiamen University, Xiamen
Gui-Fang Shao , Xiamen University, Xiamen
Hong Peng , Xiamen University, Xiamen
The gene expression data are usually provided with a large number of genes and a relatively small number of samples, which brings a lot of new challenges. Selecting those informative genes becomes the main issue in microarray data analysis. Recursive cluster elimination based on support vector machine (SVM-RCE) has shown the better classification accuracy on some microarray data sets than recursive feature elimination based on support vector machine (SVM-RFE). However, SVM-RCE is extremely time-consuming. In this paper, we propose an improved method of SVM-RCE called ISVM-RCE. ISVM-RCE first trains a SVM model with all clusters, then applies the infinite norm of weight coefficient vector in each cluster to score the cluster, finally eliminates the gene clusters with the lowest score. In addition, ISVM-RCE eliminates genes within the clusters instead of removing a cluster of genes when the number of clusters is small. We have tested ISVM-RCE on six gene expression data sets and compared their performances with SVM-RCE and linear-discriminant-analysis-based RFE (LDA-RFE). The experiment results on these data sets show that ISVM-RCE greatly reduces the time cost of SVM-RCE, meanwhile obtains comparable classification performance as SVM-RCE, while LDA-RFE is not stable.
Recursive cluster elimination, gene expression data, feature selection.

H. Peng, D. Huang, L. Ye, L. Luo, G. Shao and Q. Zhou, "Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. , pp. 122-129, 2010.
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