Grammar-guided genetic programming for fuzzy rule-based classification in credit management

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Tsakonas:2004:SIGEF,
  author =       "A. Tsakonas and G. Dounias",
  title =        "Grammar-guided genetic programming for fuzzy
                 rule-based classification in credit management",
  booktitle =    "11th meeting of the International Association for
                 Fuzzy-Set Management and Economy",
  year =         "2004",
  address =      "REGGIO CALABRIA",
  month =        "8-9 " # nov,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.149.5637",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  contributor =  "CiteSeerX",
  language =     "en",
  oai =          "oai:CiteSeerXPSU:10.1.1.149.5637",
  abstract =     "The study presents a computational intelligent
                 methodology for fuzzy rule-based classification of
                 enterprises into different categories of credit risk.
                 The presented methodology correspond to an approach to
                 the problem of classifying credit applicants, according
                 to the need for reduction of complexity, higher
                 classification accuracy, and comprehensibility of the
                 acquired decision rules. The data used are both of
                 numerical and linguistic nature and they represent a
                 real world problem, that of deciding whether a loan
                 should be granted or not, in respect to financial
                 details of customers applying for that loan, to a
                 private bank of a southern province of the European
                 Union. The techniques involved in the rule-based
                 categorization task are the inductive machine learning
                 and the type-constrained genetic programming. We
                 examine a two-step model, with a sample of 124
                 enterprises that applied for a loan, each of which is
                 described by 76 (mainly financial) decision variables,
                 and classified to one of the seven predetermined
                 classes. Special attention is given to the
                 comprehensibility and the ease of use for the acquired
                 decision rules. The application of the proposed methods
                 can make the classification task easier and may
                 minimize significantly the amount of required credit
                 data. We consider that the methodology may also give
                 the chance for the extraction of a comprehensible
                 credit management model or even the incorporation of a
                 related decision support system in banking. The overall
                 architecture of the model can be continuously retrained
                 and reformed, by adding every new credit-risk case,
                 becoming more and more accurate and robust
                 classification models over time",
  notes =        "http://gandalf.fcee.urv.es/sigef/",
}

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias

Citations