Microarray data classification based on ensemble independent component selection

Authors:
Kun-Hong Liu;Bo Li;Qing-Qiang Wu;Jun Zhang;Ji-Xiang Du;Guo-Yan Liu
Affiliations:
Software School of Xiamen University, Xiamen, Fujian, 361005, China;School of Computer Science of Technology, Wuhan University of Science and Techology, 430081, 947 Heping Road, Wuhan, Hubei, P.R. China;Software School of Xiamen University, Xiamen, Fujian, 361005, China;School of Electronic Science and Technology, Anhui University, Anhui Province, China;Department of Computer Science and Technology, Huaqiao University, Quanzhou 362021, Fujian, P.R. China;The General Surgery of the Affiliated Zhongshan Hospital of Xiamen University, The Digest Disease Research Institution of Xiamen University, Xiamen 361004, Fujian Province, China
Venue:
Computers in Biology and Medicine
Year:
2009

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Abstract

Independent component analysis (ICA) has been widely deployed to the analysis of microarray datasets. Although it was pointed out that after ICA transformation, different independent components (ICs) are of different biological significance, the IC selection problem is still far from fully explored. In this paper, we propose a genetic algorithm (GA) based ensemble independent component selection (EICS) system. In this system, GA is applied to select a set of optimal IC subsets, which are then used to build diverse and accurate base classifiers. Finally, all base classifiers are combined with majority vote rule. To show the validity of the proposed method, we apply it to classify three DNA microarray data sets involving various human normal and tumor tissue samples. The experimental results show that our ensemble method obtains stable and satisfying classification results when compared with several existing methods.