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Corruption risk analysis using semi-supervised naïve Bayes classifiers

Remis Balaniuk 1 Pierre Bessière 2 Emmanuel Mazer 3 Paulo Cobbe 1
1 UCB/MGCTI - Mestrado em Gestão do Conhecimento e da Tecnologia da Informação
2 LPPA - Laboratoire de Physiologie de la Perception et de l'Action
3 E-MOTION - Geometry and Probability for Motion and Action
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble [2007-2015]
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.
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https://hal.archives-ouvertes.fr/hal-01058580
Contributor : Pierre Bessière <>
Submitted on : Wednesday, August 27, 2014 - 11:55:33 AM
Last modification on : Friday, July 3, 2020 - 4:48:50 PM

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CNRS | INRIA | CDF | UGA | LIG | PSL | LIG_SIC_E-MOTION

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Remis Balaniuk, Pierre Bessière, Emmanuel Mazer, Paulo Cobbe. Corruption risk analysis using semi-supervised naïve Bayes classifiers. International Journal of Reasoning-based Intelligent Systems, Inderscience Enterprises Ltd, 2013, 5 (4), pp.237-245. ⟨10.1504/IJRIS.2013.058768⟩. ⟨hal-01058580⟩

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