An ensemble classifier based on kernel method for multi-situation DNA microarray data

Authors:
Xuesong Wang;Yangyang Gu;Yuhu Cheng;Ruhai Lei
Affiliations:
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China;School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu, P.R. China
Venue:
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Year:
2009

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Abstract

In order to deal with the interaction between genes effectively, a kernel technology was adopted into a subspace method in our study. A linear subspace classifier was generalized to a nonlinear kernel subspace classifier by using a kernel principle component analysis method to constitute nonlinear feature subspaces. Because DNA microarray data have characteristics of high dimension, few samples and strong nonlinearity, three types of classifiers based on kernel machine learning method were designed, i.e., support vector machine (SVM), kernel subspace classifier (KSUB-C) and kernel partial least-squares discriminant analysis (KPLS-DA). But the performances of these classifiers lie on the optimum setting of kernel functions and parameters. Therefore, to avoid the difficulty of selecting optimal kernel functions and parameters and to further improve the accuracy and generalization property of the classifiers, an ensemble classifier based on kernel method for multi-situation DNA microarray data was proposed by adopting the idea of ensemble learning. The ensemble classifier combines the classification results of the SVM, KSUB-C and KPLS-DA classifiers. Experimental results involving three public DNA microarray datasets indicate that the proposed ensemble classifier has high classification accuracy and perfect generalization property.