Title: Corruption risk analysis using semi-supervised naïve Bayes classifiers

Authors: Remis Balaniuk; Pierre Bessiere; Emmanuel Mazer; Paulo Roberto Cobbe

Addresses: MGCTI, Catholic University of Brasilia, QS 07 Lote 01 EPCT, Águas Claras, CEP: 71966-700, Taguatinga/DF, Brazil; Tribunal de Contas da União, SAFS Quadra 4, Lote 1, Brasília, DF, CEP 70042-900, Brazil ' LPPA, Collège de France, 11 Place Marcellin Berthelot, 75005 Paris, France; CNRS, E-Motion, LIG – INRIA, Inria Grenoble, Rhône-Alpes Research Centre, Inovallée 655 avenue de l'Europe, Montbonnot, 38334 Saint Ismier Cedex, France ' CNRS, E-Motion, LIG – INRIA, Inria Grenoble, Rhône-Alpes Research Centre, Inovallée 655 avenue de l'Europe, Montbonnot, 38334 Saint Ismier Cedex, France ' MGCTI, Catholic University of Brasilia, QS 07 Lote 01 EPCT, Águas Claras, CEP: 71966-700, Taguatinga/DF, Brazil; Information Technology Department, UniCEUB College, SEPN 707/907, Campus do UniCEUB, Asa Norte, Brasília, DF, CEP 70790-075, Brazil

Abstract: In this paper, we consider the application of a naïve Bayes model for the evaluation of corruption risk associated with government agencies. This model applies probabilistic classifiers to support a generic risk assessment model, allowing for more efficient and effective use of resources for the detection of corruption in government transactions, and assisting audit agencies in becoming more proactive regarding corruption detection and prevention.

Keywords: data mining; naive Bayes; corruption detection; corruption risk assessment; semi-supervised classifiers; government agencies; government transactions; audit agencies; corruption prevention.

DOI: 10.1504/IJRIS.2013.058768

International Journal of Reasoning-based Intelligent Systems, 2013 Vol.5 No.4, pp.237 - 245

Available online: 18 Jan 2014 *

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