Posterior probability support vector machines for unbalanced data

doi:10.1109/tnn.2005.857955

	Posterior probability support vector machines for unbalanced data
	Tao, Q ; Wu, GW ; Wang, FY ; Wang, J
	2005-11-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS
ISSN	1045-9227
卷号	16 期号:6 页码:1561-1573
摘要	This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter nu is obtained which is similar to the nu-SVM developed by Scholkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine nu. The main advantage of an PPSVM classifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.
关键词	Bayesian decision theory classification margin maximal margin algorithms v-SVM posterior probability support vector machines (SVMs) unbalanced data
DOI	10.1109/tnn.2005.857955
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号	WOS:000233350300021
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：82[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/2XEOYT63/10214
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Tao, Q
作者单位	1.Chinese Acad Sci, Key Lab Complex Syst & Intelligence Sci, Inst Automat, Beijing 100080, Peoples R China 2.New Star Res Inst Appl Technol, Hefei 230031, Peoples R China 3.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Bioinformat Res Grp, Beijing 100080, Peoples R China 4.Univ Arizona, Tucson, AZ 85721 USA
推荐引用方式 GB/T 7714	Tao, Q,Wu, GW,Wang, FY,et al. Posterior probability support vector machines for unbalanced data[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS,2005,16(6):1561-1573.
APA	Tao, Q,Wu, GW,Wang, FY,&Wang, J.(2005).Posterior probability support vector machines for unbalanced data.IEEE TRANSACTIONS ON NEURAL NETWORKS,16(6),1561-1573.
MLA	Tao, Q,et al."Posterior probability support vector machines for unbalanced data".IEEE TRANSACTIONS ON NEURAL NETWORKS 16.6(2005):1561-1573.