An improved multiple birth support vector machine for pattern classification

doi:10.1016/j.neucom.2016.11.006

	An improved multiple birth support vector machine for pattern classification
	Zhang, Xiekai 1,2; Ding, Shifei 1,2; Xue, Yu 3
	2017-02-15
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	225 页码:119-128
摘要	Multiple birth supportvector machine is a novel machine learning algorithm for multi-class classification, which is considered as an extension of twin support vector machine. Compared with training speeds of other multi class classifiers based on twin support vector machine, the training speed of multiple birth support vector machine is faster, especially when the number of class is large. However, one of the disadvantages of multiple birth support vector machine is that when used to deal with some datasets such as "Cross planes" datasets, multiple birth support vector machine is likely to get bad results. In order to deal with this, we propose an improved multiple birth support vector machine. We add a modified item into multiple birth support vector machine to make the variance of the distances from each samples of a given class to their hyperplanes as small as possible. To predict a new sample, our method first determines an interval for each class depending on the distances between training samples and their hyperplanes, and then classifies the new sample depending on the distances between hyperplanes and the new sample which are in the corresponding intervals. In addition, smoothing technique is applied on our model, the first time it was used in multi-class twin support vector machine. The experimental results on artificial datasets and UCI datasets show that the proposed algorithm is efficient and has good classification performance.
关键词	Support vector machine Twin support vector machine Multiple birth support vector machine Multi-class classification Smoothing technique
DOI	10.1016/j.neucom.2016.11.006
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 Higher Education Institutions ; Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000392164400012
出版者	ELSEVIER SCIENCE BV
引用统计	被引频次：36[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/2XEOYT63/7679
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 100090, 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	Zhang, Xiekai,Ding, Shifei,Xue, Yu. An improved multiple birth support vector machine for pattern classification[J]. NEUROCOMPUTING,2017,225:119-128.
APA	Zhang, Xiekai,Ding, Shifei,&Xue, Yu.(2017).An improved multiple birth support vector machine for pattern classification.NEUROCOMPUTING,225,119-128.
MLA	Zhang, Xiekai,et al."An improved multiple birth support vector machine for pattern classification".NEUROCOMPUTING 225(2017):119-128.