Naive Bayesian rough sets

Abstract. A naive Bayesian classifier is a probabilistic classifier based on Bayesian decision theory with naive independence assumptions, which is often used for ranking or constructing a binary classifier. The theory of rough sets provides a ternary classification method by approximating a set into positive, negative and boundary regions based on an equiva-lence relation on the universe. In this paper, we propose a naive Bayesian decision-theoretic rough set model, or simply a naive Bayesian rough set (NBRS) model, to integrate these two classification techniques. The con-ditional probability is estimated based on the Bayes ’ theorem and the naive probabilistic independence assumption. A discriminant function is defined as a monotonically increasing function of the conditional proba-bility, which leads to analytical and computational simplifications