[1]邹心遥,陈敬伟,姚若河.采用粒子群优化的SVM算法在数据分类中的应用[J].华侨大学学报(自然科学版),2016,37(2):171-174.[doi:10.11830/ISSN.1000-5013.2016.02.0171]
　ZOU Xinyao,CHEN Jingwei,YAO Ruohe.Application of SVM Algorithm Based on Particle Swarm Optimization in Data Classification[J].Journal of Huaqiao University(Natural Science),2016,37(2):171-174.[doi:10.11830/ISSN.1000-5013.2016.02.0171]
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采用粒子群优化的SVM算法在数据分类中的应用()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第37卷
期数:
2016年第2期
页码:
171-174
栏目:
出版日期:
2016-03-20

文章信息/Info

Title:
Application of SVM Algorithm Based on Particle Swarm Optimization in Data Classification
文章编号:
1000-5013(2016)02-0171-04
作者:
邹心遥1;  陈敬伟1;  姚若河2
1. 广东农工商职业技术学院 机电系, 广东 广州 510507;2. 华南理工大学 电子与信息学院, 广东 广州 510641
Author(s):
ZOU Xinyao1;  CHEN Jingwei1;  YAO Ruohe2
1. Department of Mechanical and Electronic, Guangdong AIB Polytechnic College, Guangzhou 510507, China; 2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510641, China
关键词:
数据分类;  支持向量机;  粒子群优化;  Iris数据集;  惩罚参数;  高斯参数
Keywords:
data classification;  support vector machine;  particle swarm optimization;  Iris data set;  penalty parameter;  Gauss parameter
分类号:
TP181
DOI:
10.11830/ISSN.1000-5013.2016.02.0171
文献标志码:
A
摘要:
针对分类数据集合线性不可分的问题,改进了支持向量机(SVM)的分类方法,构建新的分类决策函数和高斯核函数.在支持向量机关键参数的优化环节,采用粒子群算法对惩罚参数和高斯参数进行优化,设计便于操作的优化流程,并针对Iris数据集合展开实验研究.结果表明:相比于基于遗传算法优化的SVM方法,所提出的方法执行速度快、分类准确率高.
Abstract:
According to the problem that the classification data set can not be divided, the classification method of support vector machine(SVM)is improved, and the new classification decision function and Gauss kernel function are constructed. Using particle swarm algorithm to optimize the penalty parameters and Gauss parameters, the optimization process is easy to operate. To experimental study the Iris data set, the results show that compared with the SVM method based on genetic algorithm, the proposed method performs fast and has high classification accuracy.

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备注/Memo

备注/Memo:
收稿日期: 2015-12-22
通信作者: 邹心遥(1978-),女,副教授,博士,主要从事新型光电器件、物联网技术的研究.E-mail:madelinexy@163.com.
基金项目: 国家自然科学基金资助项目(61274085); 广东省大学生科技创新培育专项(PDJH2015A0718)
更新日期/Last Update: 2016-03-20