Credit Classification Using Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Anthony Brabazon and Michael O'Neill",
  title =        "Credit Classification Using Grammatical Evolution",
  journal =      "Informatica",
  year =         "2006",
  volume =       "30",
  number =       "3",
  pages =        "325--335",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Povzetek: Metoda gramaticne evolucije je
                 uporabljena za klasificiranje kreditov.",
  ISSN =         "0350-5596",
  URL =          "",
  size =         "11 pages",
  abstract =     "Grammatical Evolution (GE) is a novel data driven,
                 model induction tool, inspired by the biological
                 genetoprotein mapping process. This study provides an
                 introduction to GE, and demonstrates the methodology by
                 applying it to model the corporate bond-issuer credit
                 rating process, using information drawn from the
                 financial statements of bond-issuing firms. Financial
                 data and the associated Standard & Poor's issuer credit
                 ratings of 791 US firms, drawn from the year 1999/2000
                 are used to train and test the model. The best
                 developed model was found to be able to discriminate
                 in-sample (out-of-sample) between investment grade and
                 junk bond ratings with an average accuracy of 87.59
                 (84.92)percent across a five-fold cross validation.",
  notes =        "",

Genetic Programming entries for Anthony Brabazon Michael O'Neill