標題: Two-stage genetic programming (2SGP) for the credit scoring model
作者: Huang, JJ
Tzeng, GH
Ong, CS
運輸與物流管理系 註:原交通所+運管所
科技管理研究所
Department of Transportation and Logistics Management
Institute of Management of Technology
關鍵字: credit scoring model;artificial neural network (ANN);decision trees;rough sets;two-stage genetic programming (2SGP)
公開日期: 15-三月-2006
摘要: Credit scoring models have been widely studied in the areas of statistics, machine learning, and artificial intelligence (AI). Many novel approaches such as artificial neural networks (ANNs), rough sets, or decision trees have been proposed to increase the accuracy of credit scoring models. Since an improvement in accuracy of a fraction of a percent might translate into significant savings, a more sophisticated model should be proposed for significantly improving the accuracy of the credit scoring models. In this paper, two-stage genetic programming (2SGP) is proposed to deal with the credit scoring problem by incorporating the advantages of the IF-THEN rules and the discriminant function. On the basis of the numerical results, we can conclude that 2SGP can provide the better accuracy than other models. (c) 2005 Published by Elsevier Inc.
URI: http://dx.doi.org/10.1016/j.amc.2005.05.027
http://hdl.handle.net/11536/12487
ISSN: 0096-3003
DOI: 10.1016/j.amc.2005.05.027
期刊: APPLIED MATHEMATICS AND COMPUTATION
Volume: 174
Issue: 2
起始頁: 1039
結束頁: 1053
顯示於類別:期刊論文


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