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

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@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",
• year = "2011",
• volume = "50",
• number = "2",
• pages = "491--500",
• month = jan,
• keywords = "genetic algorithms, genetic programming, Discipulus, ROC, Data mining, Financial fraud detection, Feature selection, t-statistic, Neural networks, SVM",
• ISSN = "0167-9236",
• DOI = "doi:10.1016/j.dss.2010.11.006",
• broken = "http://www.sciencedirect.com/science/article/B6V8S-51FNP55-1/2/564cd8ff5c31e1e44b90e18f5f9ce9d4",
• 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