Credit risk analysis using a reliability-based neural network ensemble model | |
Lai, Kin Keung; Yu, Lean; Wang, Shouyang![]() | |
刊名 | ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 2
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2006 | |
卷号 | 4132页码:682-690 |
ISSN号 | 0302-9743 |
英文摘要 | 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. |
WOS研究方向 | Computer Science |
语种 | 英语 |
出版者 | SPRINGER-VERLAG BERLIN |
WOS记录号 | WOS:000241475200071 |
内容类型 | 期刊论文 |
源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/2446] ![]() |
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 |
推荐引用方式 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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