Li Jun
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China
Ding Lixin
State Key Laboratory of Software Engineering, Wuhan University, Wuhan, 430072, China
Zou Qilin
College of Computer Science and Technology, Wuhan University of Science and Technology, WUST, Wuhan, China
ABSTRACT
The classification accuracy of Support Vector Machine (SVM) depends on parameters strongly.In nature, parameter selection is a search optimization process. A Differential Evolution (DE) algorithm is a real-coding optimal algorithm based of swarm evolution. It has powerful global searching ability. But it gets into premature convergence easily. So a novel hybrid optimization algorithm based on Immunity Clone (IC) and differential evolution is proposed for parameter selection of SVM. In this algorithm, clonal selection and receptor edit mechanism are inserted into the differential evolution process. Thirteen experimental results on UCI datasets distinctly show that compared with default parameters SVM classifier, the differential evolution algorithm, the proposed algorithm has higher classification accuracy.
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How to cite this article
Li Jun, Ding Lixin and Zou Qilin, 2013. A Novel Optimization Algorithm Integrating Immunity Clone and Differential Evolution
for Parameter Selection of SVM. Information Technology Journal, 12: 7367-7372.
DOI: 10.3923/itj.2013.7367.7372
URL: https://scialert.net/abstract/?doi=itj.2013.7367.7372
DOI: 10.3923/itj.2013.7367.7372
URL: https://scialert.net/abstract/?doi=itj.2013.7367.7372
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