计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 197-203.doi: 10.11896/j.issn.1002-137X.2018.06.035

• 人工智能 • 上一篇    下一篇

基于粒子群算法的支持向量机的参数优化

陈晋音, 熊晖, 郑海斌   

  1. 浙江工业大学信息工程学院 杭州310000
  • 收稿日期:2017-04-03 出版日期:2018-06-15 发布日期:2018-07-24
  • 作者简介:陈晋音(1982-),女,副教授,主要研究方向为数据挖掘、智能计算等,E-mail:chenjinyin@zjut.edu.cn(通信作者);熊 晖(1995-),男,主要研究方向为数据挖掘及其应用;郑海斌(1995-),男,硕士生,主要研究方向为大数据分析、机器学习等
  • 基金资助:
    本文受国家自然科学青年基金(61502423),浙江省科技厅科研院专项(2016F50047)资助

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

摘要: 支持向量机(Support Vector Machine,SVM)对内部参数有着极高的依赖性,因此参数的好坏直接决定了SVM的分类效果,比如径向基核函数的参数。为了寻找出与分类问题相契合的参数,将样本数据投影到高维度特征空间,从而在特征空间中计算类内平均距离与类外中心距离之差,并将其作为参数评估的适应值;利用粒子群算法的全局寻优能力,在定义域内生成种群以代表不同的参数取值;利用粒子的随机游走来进行最优参数搜索,并将结果代入SVM进行样本训练。将所提算法与网格算法等进行了比较,结果表明所提算法的参数设定更加准确,分类准确率有显著提高,且算法复杂度并没有明显增加。

关键词: 参数优化, 粒子群优化算法, 群智能, 演化算法, 支持向量机

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

中图分类号: 

  • TP3-05
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