An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking

doi:10.1016/j.neucom.2017.07.002

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	An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking
	Zhang, Qi 1,3; Wang, Jue1,4 ; Lu, Aiguo 2; Wang, Shouyang1 ; Ma, Jian 3
刊名	NEUROCOMPUTING
	2018-01-10
卷号	272 页码:314-325
关键词	Credit risk assessment SVM Sequential minimal optimization (SMO) Four-variable working set
ISSN号	0925-2312
DOI	10.1016/j.neucom.2017.07.002
英文摘要	With rapid development of financial services and products, credit risk assessment has recently gained considerable attention in the field of financial risk management. In this paper, an improved credit risk assessment approach is presented. Based on the credit data from China Banking Regulatory Commission (CBRC), a multi-dimensional and multi-level credit risk indicator system is constructed. In particular, we present an improved sequential minimal optimization (SMO) learning algorithm, named four-variable SMO (FV-SMO), for credit risk classification model. At each iteration, it jointly selects four variables into the working set and an theorem is proposed to guarantee the analytical solution of sub-problem. The assessment is made on China credit dataset and two benchmark credit datasets from UCI database and CD-ROM database. Experimental results demonstrate FV-SMO is competitive in saving the computational cost and outperforms other five state-of-the-art classification methods in credit risk assessment accuracy. (C) 2017 Elsevier B.V. All rights reserved.
资助项目	Chinese Academy of Sciences (CAS) Foundation for Planning and Strategy Research[KACX1-YW-0906] ; Youth Innovation Promotion Association of CAS ; National Natural Science Foundation of China (NSFC)[71271202] ; NSFC project: Study on the commodity market prediction based on deep learning and Internet data
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000413821400033
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/29146]
专题	系统科学研究所
通讯作者	Wang, Jue
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Xian Shiyou Univ, Dept Appl Math, Xian 710065, Shaanxi, Peoples R China 3.City Univ Hong Kong, Dept Informat Syst, Tat Chee Ave, Kowloon, Hong Kong, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Zhang, Qi,Wang, Jue,Lu, Aiguo,et al. An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking[J]. NEUROCOMPUTING,2018,272:314-325.
APA	Zhang, Qi,Wang, Jue,Lu, Aiguo,Wang, Shouyang,&Ma, Jian.(2018).An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking.NEUROCOMPUTING,272,314-325.
MLA	Zhang, Qi,et al."An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking".NEUROCOMPUTING 272(2018):314-325.