改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

doi:10.11772/j.issn.1001-9081.2016.05.1330

计算机应用 ›› 2016, Vol. 36 ›› Issue (5): 1330-1335.DOI: 10.11772/j.issn.1001-9081.2016.05.1330

改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

张进^1,2, 丁胜^1,2, 李波^1,2

1. 武汉科技大学计算机科学与技术学院, 武汉 430065;
2. 智能媒体计算湖北省重点实验室(武汉科技大学), 武汉 430065

收稿日期:2015-10-13 修回日期:2015-12-04 出版日期:2016-05-10 发布日期:2016-05-09
通讯作者: 张进
作者简介:张进(1992-),男,湖北孝感人,硕士研究生,主要研究方向:图像处理、机器学习;丁胜(1975-),男,湖北武汉人,副教授,博士,CCF会员,主要研究方向:遥感影像分析、模式识别、机器学习;李波(1975-),男,湖北黄陂人,副教授,博士,主要研究方向:模式识别、机器学习。
基金资助:
国家自然科学基金资助项目(61273303,61572381)。

Improved particle swarm optimization algorithm for support vector machine feature selection and optimization of parameters

ZHANG Jin^1,2, DING Sheng^1,2, LI Bo^1,2

1. School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan Hubei 430065, China;
2. Hubei Province Key Laboratory of Intelligent Media Calculation(Wuhan University of Science and Technology), Wuhan Hubei 430065, China

Received:2015-10-13 Revised:2015-12-04 Online:2016-05-10 Published:2016-05-09
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61273303,61572381).

摘要/Abstract

摘要： 针对支持向量机(SVM)中特征选择和参数优化对分类精度有较大影响,提出了一种改进的基于粒子群优化(PSO)的SVM特征选择和参数联合优化算法(GPSO-SVM),使算法在提高分类精度的同时选取尽可能少的特征数目。为了解决传统粒子群算法在进行优化时易出现陷入局部最优和早熟的问题,该算法在PSO中引入遗传算法(GA)中的交叉变异算子,使粒子在每次迭代更新后进行交叉变异操作来避免这一问题。该算法通过粒子之间的不相关性指数来决定粒子之间的交叉配对,由粒子适应度值的大小决定其变异概率的大小,由此产生新的粒子进入到群体中。这样使得粒子跳出当前搜索到的局部最优位置,提高了群体的多样性,在全局范围内寻找更优值。在不同数据集上进行实验,与基于PSO和GA的特征选择和SVM参数联合优化算法相比,GPSO-SVM的分类精度平均提高了2%~3%,选择的特征数目减少了3%~15%。实验结果表明,所提算法的特征选择和参数优化效果更好。

关键词: 支持向量机, 特征选择, 参数优化, 粒子群优化算法, 遗传算法, 不相关性指数

Abstract: In view of feature selection and parameter optimization in Support Vector Machine (SVM) have great impact on the classification accuracy, an improved algorithm based on Particle Swarm Optimization (PSO) for SVM feature selection and parameter optimization (GPSO-SVM) was proposed to improve the classification accuracy and select the number of features as little as possible. In order to solve the problem that the traditional particle swarm algorithm was easy to fall into local optimum and premature maturation, the crossover and mutation operator were introduced from Genetic Algorithm (GA) that allows the particle to carry out cross and mutation operations after iteration and update to avoid the problem in PSO. The cross matching between particles was determined by the non-correlation index between particles and the mutation probability was determined by the fitness value, thereby new particles was generated into the group. By this way, the particles jump out of the previous search to the optimal position to improve the diversity of the population and to find a better value. Experiments were carried out on different data sets, compared with the feature selection and SVM parameters optimization algorithm based on PSO and GA, the accuracy of GPSO-SVM is improved by an average of 2% to 3%, and the number of selected features is reduced by 3% to 15%. The experimental result show that the features selection and parameter optimization of the proposed algorithm are better.

Key words: Support Vector Machine (SVM), feature selection, parameter optimization, Particle Swarm Optimization (PSO) algorithm, Genetic Algorithm (GA), no correlation index

中图分类号:

TP301.6

张进, 丁胜, 李波. 改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法[J]. 计算机应用, 2016, 36(5): 1330-1335.

ZHANG Jin, DING Sheng, LI Bo. Improved particle swarm optimization algorithm for support vector machine feature selection and optimization of parameters[J]. Journal of Computer Applications, 2016, 36(5): 1330-1335.

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改进的基于粒子群优化的支持向量机特征选择和参数联合优化算法

Improved particle swarm optimization algorithm for support vector machine feature selection and optimization of parameters

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