Support vector machines approach to credit assessment

CORC > 科技战略咨询研究院 > 中国科学院科技战略咨询研究院 > 中国科学院科技政策与管理科学研究所(1985年6月-2016年1月19日)

	Support vector machines approach to credit assessment
	Li, JP; Liu, JL; Xu, WX; Shi, Y
刊名	COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS
	2004
卷号	3039 页码:8,892-899
关键词	Credit Assessment Classification Support Vector Machines
ISSN号	0302-9743
英文摘要	Credit assessment has attracted lots of researchers in financial and banking industry. Recent studies have shown that Artificial Intelligence (AI) methods are competitive to statistical methods for credit assessment. This article applies support vector machines (SVM), a relatively new machine learning technique, to the credit assessment problem for better explanatory power. The structure of SVM has many computation advantages, such as special direction at a finite sample and irrelevance between the complexity of algorithm and the sample dimension. A real credit card data experiment shows that SVM method has outstanding assessment ability. Compared with the methods that are currently used by a major Chinese bank, the SVM method has a great potential superiority in predicting accuracy.
学科主题	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods
语种	英语
WOS记录号	WOS:000223079700115
公开日期	2012-11-12
内容类型	期刊论文
源URL	[http://ir.casipm.ac.cn/handle/190111/5147]
专题	科技战略咨询研究院_中国科学院科技政策与管理科学研究所(1985年6月-2015年12月)
推荐引用方式 GB/T 7714	Li, JP,Liu, JL,Xu, WX,et al. Support vector machines approach to credit assessment[J]. COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS,2004,3039:8,892-899.
APA	Li, JP,Liu, JL,Xu, WX,&Shi, Y.(2004).Support vector machines approach to credit assessment.COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS,3039,8,892-899.
MLA	Li, JP,et al."Support vector machines approach to credit assessment".COMPUTATIONAL SCIENCE - ICCS 2004, PROCEEDINGS 3039(2004):8,892-899.