Wavelet twin support vector machines based on glowworm swarm optimization

doi:10.1016/j.neucom.2016.11.026

	Wavelet twin support vector machines based on glowworm swarm optimization
	Ding, Shifei 1,2; An, Yuexuan 1; Zhang, Xiekai 1; Wu, Fulin 1; Xue, Yu 3
	2017-02-15
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	225 页码:157-163
摘要	Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently proposed wavelet twin support vector machine introduces the wavelet kernel function into twin support vector machine to make the combination of wavelet analysis techniques and twin support vector machine come true. Wavelet twin support vector machine not only expands the range of the kernel function selection, but also greatly improves the generalization ability of twin support vector machine. However, similar with twin support vector machine, wavelet twin support vector machine cannot deal with the parameter selection problem well. Unsuitable parameters reduce the classification capability of the algorithm. In order to solve the parameter selection problem in wavelet twin support vector machine, in this paper, we -use glowworm swarm optimization method to optimize the parameters of wavelet twin support vector machine and propose wavelet twin support vector machine based on glowworm swarm optimization. Wavelet twin support vector machine based on glowworm swarm optimization takes the parameters of wavelet twin support vector machine as the position information of glowworms, regards the function to Calculate the wavelet twin support vector machine classification accuracy as objective function and starts glowworm swarm optimization algorithm to update the glowworms. The optimal parameters are the position informatioh of glowworms that we get when the glowworm swarm optimal algorithm stops. Wavelet twin support vector machine based on glowworm swarm optimization determines the parameters in wavelet twin support vector machine automatically before the training process to avoid difficulty of parameter selection. Reasonable parameters promote the performance of wavelet twin support vector machine and improve the accuracy. The experimental results on benchmark datasets indicate that the proposed approach is efficient and has high classification accuracy.
关键词	Twin support vector machine Wavelet twin support vector machine Parameter optimization Glowworm swarm optimization
DOI	10.1016/j.neucom.2016.11.026
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61379101] ; National Natural Science Foundation of China[61672522] ; National Key Basic Research Program of China[2013CB329502] ; Priority Academic Program Development of Jiangsu Higer Education Institutions (PAPD) ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000392164400016
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：59[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/7649
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Ding, Shifei
作者单位	1.China Univ Min & Technol, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 3.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Ding, Shifei,An, Yuexuan,Zhang, Xiekai,et al. Wavelet twin support vector machines based on glowworm swarm optimization[J]. NEUROCOMPUTING,2017,225:157-163.
APA	Ding, Shifei,An, Yuexuan,Zhang, Xiekai,Wu, Fulin,&Xue, Yu.(2017).Wavelet twin support vector machines based on glowworm swarm optimization.NEUROCOMPUTING,225,157-163.
MLA	Ding, Shifei,et al."Wavelet twin support vector machines based on glowworm swarm optimization".NEUROCOMPUTING 225(2017):157-163.