Support vector machine based multiagent ensemble learning for credit risk evaluation

doi:10.1016/j.eswa.2009.06.083

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	Support vector machine based multiagent ensemble learning for credit risk evaluation
	Yu, Lean 1; Yue, Wuyi 2; Wang, Shouyang 1; Lai, K. K.3
	2010-03-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	37 期号:2 页码:1351-1360
摘要	In this paper, a four-stage Support vector machine (SVM) based multiagent ensemble learning approach is proposed for credit risk evaluation. In the First stage, the initial dataset is divided into two independent Subsets: training subset (in-sample data) and testing Subset (out-of-sample data) for training and verification purposes. In the second stage. different SVM learning paradigms with much dissimilarity are constructed as intelligent agents for credit risk evaluation In the third stage, multiple individual SVM agents are trained using training subsets and the corresponding evaluation results are also obtained In the final stage, all individual results produced by multiple SVM agents in the previous stage are aggregated into an ensemble result. In particular, the impact of the diversity of individual intelligent agents on the generalization performance of the SVM-based multiagent ensemble learning system is examined and analyzed For illustration. one corporate credit card application approval dataset is used to verify the effectiveness of the SVM-based multiagent ensemble learning system. (C) 2009 Elsevier Ltd All rights reserved.
关键词	Multiagent ensemble learning Support vector machine (SVM) Diversity strategy Ensemble strategy Credit risk evaluation
DOI	10.1016/j.eswa.2009.06.083
语种	英语
资助项目	National Natural Science Foundation of China[70221001] ; Chinese Academy of Sciences ; Grant-in-Aid for Science Research[19500070] ; MEXT.ORC of Japan ; NSFC/RGC Joint Research Scheme[N_CityU110/07]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000272432300054
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/10010
专题	中国科学院数学与系统科学研究院
通讯作者	Yu, Lean
作者单位	1.Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Konan Univ, Dept Intelligence & Informat, Kobe, Hyogo 6588501, Japan 3.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
推荐引用方式 GB/T 7714	Yu, Lean,Yue, Wuyi,Wang, Shouyang,et al. Support vector machine based multiagent ensemble learning for credit risk evaluation[J]. EXPERT SYSTEMS WITH APPLICATIONS,2010,37(2):1351-1360.
APA	Yu, Lean,Yue, Wuyi,Wang, Shouyang,&Lai, K. K..(2010).Support vector machine based multiagent ensemble learning for credit risk evaluation.EXPERT SYSTEMS WITH APPLICATIONS,37(2),1351-1360.
MLA	Yu, Lean,et al."Support vector machine based multiagent ensemble learning for credit risk evaluation".EXPERT SYSTEMS WITH APPLICATIONS 37.2(2010):1351-1360.