A GA based approach to improving the ICA based classification models for tumor classification

Authors:
Jian-Bing Xia-Hou;Kun-Hong Liu
Affiliations:
School of Software, Xiamen University, Xiamen, Fujian Province, China;School of Software, Xiamen University, Xiamen, Fujian Province, China
Venue:
WSEAS Transactions on Information Science and Applications
Year:
2011

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Abstract

As it has been pointed out that different ICs are of different biological significance, this paper tries to explore the IC selection problem based on a set of experiments. A regression model and a classification model, referred as penalized independent component regression (P-ICR) and ICA based Support Vector Machine (ICA+SVM), are applied to illustrate the necessity and efficiency of IC selection. A genetic algorithm (GA) is deployed to the selection process, along with an early stopping technique deployed to avoid overfitting in evolution. In particular, the individuals in the selected generation are used to construct an ensemble system to achieve higher classification accuracy. We test the two models with and without the selection methods based on three microarray datasets. The experiment results demonstrate that IC selection methods can further improve the classification accuracy of the ICA based prediction models, and the GA is more effective than the original methods.