標題: Building credit scoring models using genetic programming
作者: Ong, CS
Huang, JJ
Tzeng, GH
科技管理研究所
Institute of Management of Technology
關鍵字: credit scorings;artificial neural network (ANN);decision trees;genetic programming (GP);rough sets
公開日期: 1-七月-2005
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 to significantly improving the accuracy of the credit scoring mode. In this paper, genetic programming (GP) is used to build credit scoring models. Two numerical examples will be employed here to compare the error rate to other credit scoring models including the ANN, decision trees, rough sets, and logistic regression. On the basis of the results, we can conclude that GP can provide better performance than other models. (c) 2005 Elsevier Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/j.eswa.2005.01.003
http://hdl.handle.net/11536/13530
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2005.01.003
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 29
Issue: 1
起始頁: 41
結束頁: 47
顯示於類別:期刊論文


文件中的檔案:

  1. 000228843300004.pdf