Journal of Computer Science


Felipe Schneider Costa, Maria Marlene De Souza Pires and Silvia Modesto Nassar

DOI : 10.3844/jcssp.2013.1487.1495

Journal of Computer Science

Volume 9, Issue 11

Pages 1487-1495


The naïve Bayes classifier is considered one of the most effective classification algorithms today, competing with more modern and sophisticated classifiers. Despite being based on unrealistic (naïve) assumption that all variables are independent, given the output class, the classifier provides proper results. However, depending on the scenario utilized (network structure, number of samples or training cases, number of variables), the network may not provide appropriate results. This study uses a process variable selection, using the chi-squared test to verify the existence of dependence between variables in the data model in order to identify the reasons which prevent a Bayesian network to provide good performance. A detailed analysis of the data is also proposed, unlike other existing work, as well as adjustments in case of limit values between two adjacent classes. Furthermore, variable weights are used in the calculation of a posteriori probabilities, calculated with mutual information function. Tests were applied in both a naïve Bayesian network and a hierarchical Bayesian network. After testing, a significant reduction in error rate has been observed. The naïve Bayesian network presented a drop in error rates from twenty five percent to five percent, considering the initial results of the classification process. In the hierarchical network, there was not only a drop in fifteen percent error rate, but also the final result came to zero.


© 2013 Felipe Schneider Costa, Maria Marlene De Souza Pires and Silvia Modesto Nassar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.