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
	2018-01-10
发表期刊	NEUROCOMPUTING
ISSN	0925-2312
卷号	272 页码:314-325
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.
关键词	Credit risk assessment SVM Sequential minimal optimization (SMO) Four-variable working set
DOI	10.1016/j.neucom.2017.07.002
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence
WOS记录号	WOS:000413821400033
出版者	ELSEVIER SCIENCE BV
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/29147
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.