The Impact of Pruning BayesFuzzy Rule Set

The use of Bayesian Network Classifiers (BCs) combined with the Fuzzy rule model to explain the learned BCs have been previously presented as the BayesFuzzy approach. This paper follows along BayesFuzzy lines of investigation aiming at improving the comprehensibility of a BC model and enhancing BayesFuzzy results by combining new pruning methods. In order to improve BayesFuzzy performance, in addition to the Markov Blanket-based pruning idea used by BayesFuzzy, two other pruning methods are proposed, implemented and empirically evaluated. The first pruning method is based on the conditional probability estimates given by the BC and the second one is the well-known post-rule pruning approach, usually used to prune rules extracted from decision trees. Also, three different Bayesian Networks induction algorithms, namely IC, K2 and Nai¿ve-Bayes, as well as, the C4.5 Decision Tree induction algorithms are employed in the empirical comparative analysis performed in the experiments. The obtained results reveal that BayesFuzzy combined with the new pruning methods can bring comprehensibility enhancements.