DocumentCode :
3367882
Title :
Differential Evolution Based Parameters Selection for Support Vector Machine
Author :
Li Jun ; Ding Lixin ; Xing Ying
Author_Institution :
State Key Lab. of Software Eng., Wuhan Univ., Wuhan, China
fYear :
2013
fDate :
14-15 Dec. 2013
Firstpage :
284
Lastpage :
288
Abstract :
This paper addresses the problem of SVM parameter optimization. The authors propose an SVM classification system based on differential evolution(DE) to improve the generalization performance of the SVM classifier. For this purpose, the authors have optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function. The experiments are conducted on the basis of benchmark dataset. The obtained results clearly confirm the superiority of the DE-SVM approach compared to default parameters SVM classifier and suggest that further substantial improvements in terms of classification accuracy can be achieved by the proposed DE-SVM classification system.
Keywords :
evolutionary computation; generalisation (artificial intelligence); optimisation; pattern classification; support vector machines; DE-SVM classification system; SVM classifier design optimization; SVM parameter optimization; differential evolution; generalization performance improvement; parameters selection; support vector machine; Accuracy; Educational institutions; Optimization; Sociology; Statistics; Support vector machines; Vectors; differential evolution(DE); high-dimentional classfication; optimization; support vector machine(SVM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence and Security (CIS), 2013 9th International Conference on
Conference_Location :
Leshan
Print_ISBN :
978-1-4799-2548-3
Type :
conf
DOI :
10.1109/CIS.2013.67
Filename :
6746403
Link To Document :
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