Computer Science ›› 2018, Vol. 45 ›› Issue (6): 197-203.doi: 10.11896/j.issn.1002-137X.2018.06.035

• Artificial Intelligence • Previous Articles     Next Articles

Parameters Optimization for SVM Based on Particle Swarm Algorithm

CHEN Jin-yin, XIONG Hui, ZHENG Hai-bin   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310000,China
  • Received:2017-04-03 Online:2018-06-15 Published:2018-07-24

Abstract: Support vector machine has high dependence for Hyper-parameters,so parameter setting determines the classification of SVM such as the parameters of RBF kernel function.In order to select proper parameters corresponding to the classification problem,the data set is mapped to the high-dimensional feature space to calculate average distance between classes and the distance between two centers.The difference between results is taken as the fitness value of parameter assessment.Through global optimization ability of particle swarm algorithm,population representing different parameters are generated in the defined domain.The optimal parameter search is performed by random walk of particles,and the results are taken into SVM for training.Compared with grid algorithm,the parameters setting of the proposed algorithm is more accurate,the classification accuracy is significantly improved,and the complexity of the algorithm doesn’t increase.

Key words: Evolutionary algorithm, Parameter optimization, Particle swarm optimization algorithm, Support vector machine, Swarm intelligent

CLC Number: 

  • TP3-05
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