Bayesian Models to Assess Risk of Corruption of Federal Management Units

This paper presents a data mining project that generated Bayesian models to assess risk of corruption of federal management units. With thousands of extracted features related to corruptibility, the data were processed using techniques like correlation analysis and variance per class. We also compared two different discretization methods: Minimum Description Length Principle (MDLP) and Class-Attribute Contingency Coefficient (CACC). The feature selection process used Adaptive Lasso. To choose our final model we evaluated three different algorithms: Naı̈ve Bayes, Tree Augmented Naı̈ve Bayes, and Attribute Weighted Naı̈ve Bayes. Finally, we analyzed the rules generated by the model in order to support knowledge discovery.