Combining SVM classifiers using genetic fuzzy systems based on AUC for gene expression data analysis

Authors:
Xiujuan Chen;Yichuan Zhao;Yan-Qing Zhang;Robert Harrison
Affiliations:
Department of Computer Science, Georgia State University, Atlanta, GA;Department of Mathematics and Statistics, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA;Department of Computer Science, Georgia State University, Atlanta, GA
Venue:
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Year:
2007

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Abstract

Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve (AUC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUCs. Our experiments show that AUC-based genetic fuzzy SVM fusion model produces not only better AUC but also better accuracy than individual SVM classifiers.