https://scholars.lib.ntu.edu.tw/handle/123456789/105417
標題: | Two-stagegenetic programming (2SGP) for the credit scoring model | 作者: | Huang, Jih-Jeng Tzeng, Gwo-Hshiung Ong, Chorng-Shyong |
關鍵字: | Artificial neural network (ANN); Credit scoring model; Decision trees; Rough sets; Two-stage genetic programming (2SGP) | 公開日期: | 2006 | 卷: | 174 | 期: | 2 | 起(迄)頁: | 1039-1053 | 來源出版物: | Applied Mathematics and Computation | 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. |
URI: | http://ntur.lib.ntu.edu.tw//handle/246246/84962 https://www.scopus.com/inward/record.uri?eid=2-s2.0-33644600030&doi=10.1016%2fj.amc.2005.05.027&partnerID=40&md5=df1768173a866fc2202e681cb6776822 |
ISSN: | 00963003 | SDG/關鍵字: | Artificial intelligence; Functions; Learning systems; Neural networks; Rough set theory; Trees (mathematics); Credit scoring models; Decision trees; Rough sets; Two-stage genetic programming (2SGP); Computer programming |
顯示於: | 資訊管理學系 |
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