Computer Science ›› 2020, Vol. 47 ›› Issue (2): 37-43.doi: 10.11896/jsjkx.190100092

• Database & Big Data & Data Science • Previous Articles     Next Articles

Support Vector Machine Model Based on Grey Wolf Optimization Fused Asymptotic

WU Yu-kun,XIAO Jie,Wei William LEE,LOU Ji-lin   

  1. (College of Computer Science & Technology,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2019-01-12 Online:2020-02-15 Published:2020-03-18
  • About author:WU Yu-kun,born in 1980,doctorial student,is member of China Computer Federation (CCF).His main research interests include machine learning and big data;Wei William LEE,born in 1958,Ph.D,professor.His main research interests include big data,block chain,IOT and smart city development.
  • Supported by:
    This work supported by the National Natural Science Foundation of China (61502422) and Natural Science Foundation of Zhejiang Province (LY18F020028).

Abstract: The development of big data requires higher accuracy of data classification.The wide application of support vector machine (SVM) requires an efficient method to construct an SVM classifier with strong classification ability.The kernel parameter,penalty parameter and feature subsets of dataset have an important impact on the complexity and prediction accuracy of the model.In order to improve the classification performance of SVM,the asymptotic of SVM was integrated into the gray wolf optimization (GWO) algorithm,and a new SVM classifier model was proposed.The model optimizes feature selection and parameter optimization of SVM at the same time.The new grey wolf individual integrating the asymptotic property of SVM directs the search space of grey wolf optimization algorithm to the optimal region in super-parameter space,and can obtain the optimal solution faster.In addition,a new fitness function,which combines the classification accuracy obtained from the method,the number of chosen features and the number of support vectors,was proposed.The new fitness function and GWO fused asymptotic lead the search to the optimal solution.This paper used several classical datasets on UCI to verify the proposed model.Compared with the grid search algorithm,the gray wolf optimization algorithm without asymptotic convergence and other methods in the literature,the classification accuracy of the proposed algorithm has different degrees of improvement on different data sets.The experimental results show that the proposed algorithm can find the optimal parameters and the smallest feature subset of SVM,with higher classification accuracy and less average processing time.

Key words: Asymptotic, Feature selection, Gray wolf optimization algorithm, Parameters optimization, Support vector machines

CLC Number: 

  • TP391
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