A New Approach to Improving ICA-Based Models for the Classification of Microarray Data

Authors:
Kun-Hong Liu;Bo Li;Jun Zhang;Ji-Xiang Du
Affiliations:
School of Software, Xiamen University, Xiamen, China 361005;School of Computer Science of Technology, Wuhan University of Science and Techology, Wuhan, P.R. China 430081;School of Electronic Science and Technology, Anhui University,;Department of Computer Science and Technology, Huaqiao University, Quanzhou, P.R. China 362021
Venue:
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Year:
2009

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Abstract

Inspired by the idea of ensemble feature selection, we design an ICA based ensemble learning system to fully utilize the difference among different IC sets. Firstly, some IC sets are generated by different ICA transformations. A multi-objective genetic algorithm (MOGA) is then designed to select different biologically significant IC subsets from these IC sets, which are applied to build base classifiers. In addition, a global-recording technique is designed to record the best IC subsets of each IC set discovered by the MOGA into a global-recording list. When MOGA stops, all individuals in the list are deployed to train base classifiers. The base classifiers generated by these schemes are fused by the majority vote rule. Three microarray datasets are used to test the ensemble systems, and the corresponding results demonstrate that two ensemble schemes can improve the performance of the ICA based classification model.