A genetic programming approach for bankruptcy prediction using a highly unbalanced database

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@InProceedings{alfaro-cid:evows07,
  author =       "Eva Alfaro-Cid and Ken Sharman and 
                 Anna I. Esparcia-Alc\`azar",
  title =        "A genetic programming approach for bankruptcy
                 prediction using a highly unbalanced database",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoWorkshops2007: {EvoCOMNET}, {EvoFIN}, {EvoIASP},
                 {EvoInteraction}, {EvoMUSART}, {EvoSTOC},
                 {EvoTransLog}",
  year =         "2007",
  month =        "11-13 " # apr,
  editor =       "Mario Giacobini and Anthony Brabazon and 
                 Stefano Cagnoni and Gianni A. {Di Caro} and Rolf Drechsler and 
                 Muddassar Farooq and Andreas Fink and 
                 Evelyne Lutton and Penousal Machado and Stefan Minner and 
                 Michael O'Neill and Juan Romero and Franz Rothlauf and 
                 Giovanni Squillero and Hideyuki Takagi and A. Sima Uyar and 
                 Shengxiang Yang",
  series =       "LNCS",
  volume =       "4448",
  publisher =    "Springer Verlag",
  address =      "Valencia, Spain",
  pages =        "169--178",
  keywords =     "genetic algorithms, genetic programming, SVM",
  isbn13 =       "978-3-540-71804-8",
  DOI =          "doi:10.1007/978-3-540-71805-5_19",
  abstract =     "in this paper we present the application of a genetic
                 programming algorithm to the problem of bankruptcy
                 prediction. To carry out the research we have used a
                 database of Spanish companies. The database has two
                 important drawbacks: the number of bankrupt companies
                 is very small when compared with the number of healthy
                 ones (unbalanced data) and a considerable number of
                 companies have missing data. For comparison purposes we
                 have solved the same problem using a support vector
                 machine. Genetic programming has achieved very
                 satisfactory results, improving those obtained with the
                 support vector machine.",
  notes =        "EvoWorkshops2007",
}

Genetic Programming entries for Eva Alfaro-Cid Kenneth C Sharman Anna Esparcia-Alcazar

Citations