Detection of financial statement fraud and feature selection using data mining techniques
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Aamodt, 1994, Case-based reasoning: foundational issues, methodological variations, and system approaches, Artificial Intelligence Communications, 7, 39
Altman, 1968, Financial ratios, discriminant analysis and prediction of corporate bankruptcy, The Journal of Finance, 23, 589, 10.2307/2978933
Beaver, 1966, Financial ratios as predictors of failure, Journal of Accounting Research, 4, 71, 10.2307/2490171
Berrar, 2003, Multiclass cancer classification using gene expression profiling and probabilistic neural networks, vol. 8, 5
Bose, 2008
Brooks
Busta, 1998, Using Benford's law and neural networks as a review procedure, Managerial Auditing Journal, 13, 356, 10.1108/02686909810222375
Calderon, 2002, A roadmap for future neural networks research in auditing and risk assessment, International Journal of Accounting Information Systems, 3, 203, 10.1016/S1467-0895(02)00068-4
M. Cecchini, H. Aytug, G.J. Koehler, and P. Pathak. Detecting Management Fraud in Public Companies. http://warrington.ufl.edu/isom/docs/papers/DetectingManagementFraudInPublicCompanies.pdf
Cerullo, 1999, Using neural networks to predict financial reporting fraud: Part 1, Computer Fraud & Security, 5, 14
Chen, 2008, A maximum entropy approach to feature selection in knowledge-based authentication, Decision Support Systems, 46, 388, 10.1016/j.dss.2008.07.008
Deshmukh, 1998, A rule-based fuzzy reasoning system for assessing the risk of management fraud, International Journal of Intelligent Systems in Accounting, Finance & Management, 7, 223, 10.1002/(SICI)1099-1174(199812)7:4<223::AID-ISAF158>3.0.CO;2-I
Fanning, 1998, Neural network detection of management fraud using published financial data, International Journal of Intelligent Systems in Accounting, Finance, and Management, 7, 21, 10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K
Faraoun, 2006, Genetic programming approach for multi-category pattern classification applied to network intrusion detection, International Journal of Computational Intelligence and Applications, 6, 77, 10.1142/S1469026806001812
Feroz, 2000, The efficacy of red flags in predicting the SEC's targets: an artificial neural networks approach, International Journal of Intelligent Systems in Accounting, Finance, and Management, 9, 145, 10.1002/1099-1174(200009)9:3<145::AID-ISAF185>3.0.CO;2-G
Fu, 2006, Feature similarity based redundancy reduction for gene selection
Huang, 2008, An investigation of Zipf's Law for fraud detection, Decision Support Systems, 46, 70, 10.1016/j.dss.2008.05.003
Ivakhnenko, 1966, The group method of data handling—a rival of the method of stochastic approximation, Soviet Automatic Control, 13, 43
Kim, 2006, Toward a successful CRM: variable selection, sampling, and ensemble, Decision Support Systems, 41, 542, 10.1016/j.dss.2004.09.008
Kirkos, 2007, Data mining techniques for the detection of fraudulent financial statement, Expert Systems with Applications, 32, 995, 10.1016/j.eswa.2006.02.016
KNIME 2.0.0. http://www.knime.org
Koskivaara, 2000, Different pre-processing models for financial accounts when using neural networks for auditing, vol. 1, 326
Koskivaara, 2004, Artificial neural networks in auditing: state of the art, The ICFAI Journal of Audit Practice, 1, 12
Kotsiantis, 2006, Forecasting fraudulent financial statements using data mining, International Journal of Computational Intelligence, 3, 104
Koza, 1992
Liu, 2002, A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns, Genome Informatics, 13, 51
Magnusson, 2005, The language of quarterly reports as an indicator of change in the company's financial status, Information & Management, 42, 561, 10.1016/S0378-7206(04)00072-2
Mladenic, 2003, Feature selection on hierarchy of web documents, Decision Support Systems, 35, 45, 10.1016/S0167-9236(02)00097-0
Neucom
Neuroshell 2.0, Ward Systems Inc. http://www.wardsystems.com.
Pacheco, 1996, A hybrid intelligent system applied to financial statement analysis, vol. 2, 1007
Panik, 2009
Piramuthu, 2003, On learning to predict web traffic, Decision Support Systems, 35, 213, 10.1016/S0167-9236(02)00107-0
Ramamoorti, 1999, Risk assessment in internal auditing: a neural network approach, International Journal of Intelligent Systems in Accounting, Finance & Management, 8, 159, 10.1002/(SICI)1099-1174(199909)8:3<159::AID-ISAF169>3.0.CO;2-W
Ramos, 2003, Auditor's responsibility for fraud detection, Journal of Accountancy, 195, 28
Rumelhart, 1986
Selekwa, 2005, Setting up a probabilistic neural network for classification of highway vehicles, International Journal of Computational Intelligence and Applications, 5, 411, 10.1142/S1469026805001702
Sohl, 1995, A neural network approach to forecasting model selection, Information & Management, 29, 297, 10.1016/0378-7206(95)00033-4
Spathis, 2002, Detecting falsified financial statements: a comparative study using multicriteria analysis and multivariate statistical techniques, European Accounting Review, 11, 509, 10.1080/0963818022000000966
Vapnik, 1998, Adaptive and learning systems for signal processing
Williams, 2009, Mine classification with imbalanced data, IEEE Geoscience and Remote Sensing Letters, 6, 528, 10.1109/LGRS.2009.2021964