摘要
针对不平衡数据集下,传统的模糊支持向量机(Fussy support vector machine,FSVM)算法分类效果不够明显,引入的参数未做优化等缺点,本文提出一种基于粒子群算法(Particle swarmoptimization,PSO)优化的改进模糊支持向量机算法,即PSO-DEC-IFSVM算法。该算法首先综合考虑训练样本到其类中心的间距、样本周围的紧密度以及样本的信息量设计模糊隶属度函数,然后将此改进的模糊支持向量机与不同惩罚因子(Different error costs,DEC)算法相结合得到DEC-IFSVM算法,最后利用粒子群算法对DEC-IFSVM算法引入的参数进行优化。实验证明:对于UCI公共数据集中的Pima等6种不平衡数据集,相比已有的FSVM及其改进算法,PSO-DEC-IFSVM算法具有更好的正负类分类效果以及更强的鲁棒性。
For the unbalanced datasets,the traditional fuzzy support vector machine(FSVM)algorithm classification effect is not obvious,and the introduced parameters are not optimized.Therefore,this paper proposes an improved fuzzy support vector machine(IFSVM)algorithm based on particle swarm optimization(PSO)algorithm,i.e.PSO-DEC-IFSVM algorithm.First,the algorithm is used to design fuzzy membership function considering the distance from training sample to its center,the tightness around the sample and the amount of information of the sample,and then IFSVM algorithm is combined with different error costs(DEC)algorithm for obtaining the DEC-IFSVM algorithm.Finally the PSO algorithm is used to optimize the introduced parameters in the DEC-IFSVM algorithm.Experiments show that the PSO-DEC-IFSVM algorithm has better positive and negative classification effect and stronger robustness than the existing FSVM algorithm and its improved algorithm for the six unbalanced data sets,such as Pima in UCI public data set.
作者
魏建安
黄海松
康佩栋
Wei Jianan;Huang Haisong;Kang Peidong(Key Laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang,550025,China)
出处
《数据采集与处理》
CSCD
北大核心
2019年第4期723-735,共13页
Journal of Data Acquisition and Processing
基金
贵州工业攻关重点（黔科合GZ字[2015]3009）资助项目
贵州省自然科学基金（黔科合J字[2015]2043）资助项目
贵州省重大专项（黔科合JZ字[2014]2001）资助项目
贵州省教育厅（黔教合协同创新字[2015]02）资助项目
贵州大学研究生创新基金（研理工2017037）资助项目
关键词
不平衡数据分类
改进模糊支持向量机
样本信息量
粒子群算法
参数寻优
unbalanced data classification
improved fuzzy support vector machine
sample information
particle swarm optimization
parameter optimization
