Building credit scoring models using genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Chorng-Shyong Ong and Jih-Jeng Huang and 
                 Gwo-Hshiung Tzeng",
  title =        "Building credit scoring models using genetic
  journal =      "Expert Systems with Applications",
  year =         "2005",
  volume =       "29",
  pages =        "41--47",
  number =       "1",
  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.",
  owner =        "wlangdon",
  URL =          "",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Credit
                 scoring, Artificial neural network (ANN), Decision
                 trees, Rough sets",
  DOI =          "doi:10.1016/j.eswa.2005.01.003",

Genetic Programming entries for Chorng-Shyong Ong Jih-Jeng Huang Gwo-Hshiung Tzeng