Detection of financial statement fraud and feature selection using data mining techniques

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@Article{Ravisankar2011491,
  author =       "P. Ravisankar and V. Ravi and G. Raghava Rao and 
                 I. Bose",
  title =        "Detection of financial statement fraud and feature
                 selection using data mining techniques",
  journal =      "Decision Support Systems",
  volume =       "50",
  number =       "2",
  pages =        "491--500",
  year =         "2011",
  ISSN =         "0167-9236",
  DOI =          "doi:10.1016/j.dss.2010.11.006",
  URL =          "http://www.sciencedirect.com/science/article/B6V8S-51FNP55-1/2/564cd8ff5c31e1e44b90e18f5f9ce9d4",
  keywords =     "genetic algorithms, genetic programming, Discipulus,
                 ROC, Data mining, Financial fraud detection, Feature
                 selection, t-statistic, Neural networks, SVM",
  size =         "10 pages",
  abstract =     "Recently, high profile cases of financial statement
                 fraud have been dominating the news. This paper uses
                 data mining techniques such as Multilayer Feed Forward
                 Neural Network (MLFF), Support Vector Machines (SVM),
                 Genetic Programming (GP), Group Method of Data Handling
                 (GMDH), Logistic Regression (LR), and Probabilistic
                 Neural Network (PNN) to identify companies that resort
                 to financial statement fraud. Each of these techniques
                 is tested on a dataset involving 202 Chinese companies
                 and compared with and without feature selection. PNN
                 outperformed all the techniques without feature
                 selection, and GP and PNN outperformed others with
                 feature selection and with marginally equal
                 accuracies.",
  notes =        "p499 'PNN was the top performer followed by GP'",
}

Genetic Programming entries for P Ravi Shankar Vadlamani Ravi G Raghava Rao Indranil Bose

Citations