%0 Journal Article %1 Ong:2005:ESA %A Ong, Chorng-Shyong %A Huang, Jih-Jeng %A Tzeng, Gwo-Hshiung %D 2005 %J Expert Systems with Applications %K (ANN), Artificial Credit Decision Rough algorithms, genetic network neural programming, scoring, sets trees, %N 1 %P 41--47 %R doi:10.1016/j.eswa.2005.01.003 %T Building credit scoring models using genetic programming %U http://www.sciencedirect.com/science/article/B6V03-4F91Y8H-1/2/5116a9e3777103e0a563afc38e92e23a %V 29 %X 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. 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.

@article{Ong:2005:ESA, abstract = {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. 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.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Ong, Chorng-Shyong and Huang, Jih-Jeng and Tzeng, Gwo-Hshiung}, biburl = {https://www.bibsonomy.org/bibtex/28ef1f99fb86b220d02a1ddda4b05c777/brazovayeye}, doi = {doi:10.1016/j.eswa.2005.01.003}, interhash = {262f597d4f0816bf06e785df9e9f603c}, intrahash = {8ef1f99fb86b220d02a1ddda4b05c777}, journal = {Expert Systems with Applications}, keywords = {(ANN), Artificial Credit Decision Rough algorithms, genetic network neural programming, scoring, sets trees,}, month = {July}, number = 1, owner = {wlangdon}, pages = {41--47}, timestamp = {2008-06-19T17:49:01.000+0200}, title = {Building credit scoring models using genetic programming}, url = {http://www.sciencedirect.com/science/article/B6V03-4F91Y8H-1/2/5116a9e3777103e0a563afc38e92e23a}, volume = 29, year = 2005 }