Abstract
A Bayesian network (BN) is a formalism for representing and reasoning about uncertain domains. In BNs the knowledge is represented by a combination of a graph-based structure and probability theory. A particular type of BN known as Bayesian Classifier (BC) aims at classifying a given instance into a discrete class. BCs have been intensively used for knowledge modeling in many different applications and have been the focus of many works related to data mining. Data mining tasks are usually applied to real domains having large number of variables. In such domains, the classifiers tend to be large and complex and consequently are not so easily understood by human beings. This paper proposes an approach for promoting the understandability of the knowledge represented by a BC, by translating it into a more convenient and easily understandable form of representation, that of classification rules. The proposed method named BayesRule uses the concept of Markov-Blanket to obtain a reduced set of rules in relation to both, the number of rules and the number of conditions in the antecedent of a rule. Experiments using seven knowledge domains show that the reduced set of rules extracted from a BC can be smaller and still maintain the BC classification accuracy.