CSpace  > 系统科学研究所
An improved SMO algorithm for financial credit risk assessment - Evidence from China's banking
Zhang, Qi1,3; Wang, Jue1,4; Lu, Aiguo2; Wang, Shouyang1; Ma, Jian3
2018-01-10
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume272Pages:314-325
AbstractWith 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.
KeywordCredit risk assessment SVM Sequential minimal optimization (SMO) Four-variable working set
DOI10.1016/j.neucom.2017.07.002
Language英语
Funding ProjectChinese 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 Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000413821400033
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29147
Collection系统科学研究所
Affiliation1.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
Recommended Citation
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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Zhang, Qi]'s Articles
[Wang, Jue]'s Articles
[Lu, Aiguo]'s Articles
Baidu academic
Similar articles in Baidu academic
[Zhang, Qi]'s Articles
[Wang, Jue]'s Articles
[Lu, Aiguo]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Zhang, Qi]'s Articles
[Wang, Jue]'s Articles
[Lu, Aiguo]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.