Particle swarm optimization for parameter determination and feature selection of support vector machines
Particle swarm optimization for parameter determination and feature selection of support vector machines
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DOI：
10.1016/j.eswa.2007.08.088
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发表时间：
2008-11-01
影响因子：
8.5
通讯作者：
Lee, Zne-Jung
中科院分区：
文献类型：
--
作者：
Lin, Shih-Wei;Ying, Kuo-Ching;Lee, Zne-Jung
支持向量机是一种广泛应用的模式分类方法。支持向量机训练过程中的核参数设置，沿着特征选择，显著影响分类精度。本研究同时确定参数值，同时发现一个子集的功能，而不会降低SVM分类精度。提出了一种基于粒子群优化（PSO）的支持向量机参数确定和特征选择方法（PSO + SVM），并利用几个公开数据集计算分类准确率，对改进的PSO + SVM方法进行了评价。所开发的方法进行了比较，网格搜索，这是一种传统的方法搜索参数值，和其他方法。实验结果表明，该方法的分类准确率优于网格搜索等方法，且PSO + SVM方法与GA + SVM方法具有相似的分类效果。因此，PSO + SVM的方法是有价值的参数确定和特征选择的支持向量机。(C)2007爱思唯尔有限公司保留所有权利。
Support vector machine (SVM) is a popular pattern classification method with many diverse applications. Kernel parameter setting ill the SVM training procedure, along with the feature selection, significantly influences the classification accuracy. This study simultaneously determines the parameter values while discovering a subset of features, without reducing SVM classification accuracy. A particle swarm optimization (PSO) based approach for parameter determination and feature selection of the SVM, termed PSO + SVM, is developed.Several public datasets are employed to calculate the classification accuracy rate in order to evaluate the developed PSO + SVM approach. The developed approach was compared with grid search, which is a conventional method of searching parameter values, and other approaches. Experimental results demonstrate that the classification accuracy rates of the developed approach surpass those of grid search and many other approaches, and that the developed PSO + SVM approach has a similar result to GA + SVM. Therefore, the PSO + SVM approach is valuable for parameter determination and feature selection in an SVM. (C) 2007 Elsevier Ltd. All rights reserved.