Bankruptcy prediction with neural logic networks by means of grammar-guided genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Athanasios Tsakonas and George Dounias and 
                 Michael Doumpos and Constantin Zopounidis",
  title =        "Bankruptcy prediction with neural logic networks by
                 means of grammar-guided genetic programming",
  journal =      "Expert Systems With Applications",
  year =         "2006",
  volume =       "30",
  number =       "3",
  pages =        "449--461",
  month =        apr,
  note =         "Intelligent Information Systems for Financial
  keywords =     "genetic algorithms, genetic programming, Bankruptcy,
                 Neural logic networks, Grammar-Guided genetic
                 programming, Cellular encoding",
  DOI =          "doi:10.1016/j.eswa.2005.10.009",
  abstract =     "The paper demonstrates the efficient use of hybrid
                 intelligent systems for solving the classification
                 problem of bankruptcy. The aim of the study is to
                 obtain classification schemes able to predict business
                 failure. Previous attempts to form efficient
                 classifiers for the same problem using intelligent or
                 statistical techniques are discussed throughout the
                 paper. The application of neural logic networks by
                 means of genetic programming is proposed. This is an
                 advantageous approach enabling the interpretation of
                 the network structure through set of expert rules,
                 which is a desirable feature for field experts. These
                 evolutionary neural logic networks are consisted of an
                 innovative hybrid intelligent methodology, by which
                 evolutionary programming techniques are used for
                 obtaining the best possible topology of a neural logic
                 network. The genetic programming process is guided
                 using a context-free grammar and indirect encoding of
                 the neural logic networks into the genetic programming
                 individuals. Indicative classification results are
                 presented and discussed in detail in terms of both,
                 classification accuracy and solution

Genetic Programming entries for Athanasios D Tsakonas Georgios Dounias Michael Doumpos Constantin Zopounidis