Corruption risk analysis using semi-supervised naïve Bayes classifiers Online publication date: Sat, 18-Jan-2014
by Remis Balaniuk; Pierre Bessiere; Emmanuel Mazer; Paulo Roberto Cobbe
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 5, No. 4, 2013
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.
Online publication date: Sat, 18-Jan-2014
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