Selective SVMs ensemble driven by immune clonal algorithm

Authors:
Xiangrong Zhang;Shuang Wang;Tan Shan;Licheng Jiao
Affiliations:
National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing, Institute of Intelligent Information Processing, Xidian University, Xi'an, China
Venue:
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Year:
2005

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Abstract

A selective ensemble of support vector machines (SVMs) based on immune clonal algorithm (ICA) is proposed for the case of classification. ICA, a new intelligent computation method simulating the natural immune system, characterized by rapid convergence to global optimal solutions, is employed to select a suitable subset of the trained component SVMs to make up of an ensemble with high generalization performance. The experimental results on some popular datasets from UCI database show that the selective SVMs ensemble outperforms a single SVM and traditional ensemble method that ensemble all the trained component SVMs.