Classification Rule Mining for Automatic Credit Approval Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4534

  author =       "Sum Sakprasat and Mark C. Sinclair",
  title =        "Classification Rule Mining for Automatic Credit
                 Approval Using Genetic Programming",
  booktitle =    "2007 IEEE Congress on Evolutionary Computation",
  year =         "2007",
  editor =       "Dipti Srinivasan and Lipo Wang",
  pages =        "548--555",
  address =      "Singapore",
  month =        "25-28 " # sep,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  ISBN =         "1-4244-1340-0",
  file =         "1223.pdf",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CEC.2007.4424518",
  abstract =     "Automatic credit approval is important for the
                 efficient processing of credit applications. Eight
                 different genetic programming (GP) approaches for the
                 classification rule mining of a credit card application
                 dataset are investigated, using both a Booleanizing
                 technique and strongly-typed GP. In addition, the use
                 of GP for missing value handling is evaluated. Overall,
                 on the Australian Credit Approval dataset, those GP
                 approaches that had poorer classification correctness
                 on the training data often proved better at
                 generalising for the test set.",
  notes =        "CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C",

Genetic Programming entries for Sum Sakprasat Mark C Sinclair