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Credit risk analysis using a reliability-based neural network ensemble model
Lai, Kin Keung; Yu, Lean; Wang, Shouyang; Zhou, Ligang
2006
发表期刊ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2
ISSN0302-9743
卷号4132页码:682-690
摘要Credit risk analysis is an important topic in the financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. An accurate estimation of credit risk could be transformed into a more efficient use of economic capital. In this study, we try to use a triple-phase neural network ensemble technique to design a credit risk evaluation system to discriminate good creditors from bad ones. In this model, many diverse neural network models are first created. Then an uncorrelation maximization algorithm is used to select the appropriate ensemble members. Finally, a reliability-based method is used for neural network ensemble. For further illustration, a publicly credit dataset is used to test the effectiveness of the proposed neural ensemble model.
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Theory & Methods
WOS记录号WOS:000241475200071
出版者SPRINGER-VERLAG BERLIN
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/2447
专题系统科学研究所
通讯作者Lai, Kin Keung
作者单位1.Hunan Univ, Coll Business Adm, Changsha 410082, Peoples R China
2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100080, Peoples R China
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GB/T 7714
Lai, Kin Keung,Yu, Lean,Wang, Shouyang,et al. Credit risk analysis using a reliability-based neural network ensemble model[J]. ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2,2006,4132:682-690.
APA Lai, Kin Keung,Yu, Lean,Wang, Shouyang,&Zhou, Ligang.(2006).Credit risk analysis using a reliability-based neural network ensemble model.ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2,4132,682-690.
MLA Lai, Kin Keung,et al."Credit risk analysis using a reliability-based neural network ensemble model".ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2 4132(2006):682-690.
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