Microarray data classification based on ensemble independent component selection.
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.
Liu
Kun-Hong
KH
Software School of Xiamen University, Xiamen, Fujian, 361005, China. lkhqz@163.com
Li
Bo
B
Wu
Qing-Qiang
QQ
Zhang
Jun
J
Du
Ji-Xiang
JX
Liu
Guo-Yan
GY
D000465:Algorithms; D008962:Models, Theoretical; D009369:Neoplasms / Q000235:genetics / Q000473:pathology; D020411:Oligonucleotide Array Sequence Analysis