Two-stage genetic programming (2SGP) for the credit scoring model

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

  author =       "Jih-Jeng Huang and Gwo-Hshiung Tzeng and 
                 Chorng-Shyong Ong",
  title =        "Two-stage genetic programming {(2SGP)} for the credit
                 scoring model",
  journal =      "Applied Mathematics and Computation",
  year =         "2006",
  volume =       "174",
  number =       "2",
  pages =        "1039--1053",
  month =        "15 " # mar,
  keywords =     "genetic algorithms, genetic programming, Credit
                 scoring model, Artificial neural network (ANN),
                 Decision trees, Rough sets, Two-stage genetic
                 programming (2SGP)",
  URL =          "",
  DOI =          "doi:10.1016/j.amc.2005.05.027",
  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 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.",

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