Naive Bayesian Rough Sets

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 equivalence 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 conditional 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 probability, which leads to analytical and computational simplifications. Key words: three-way decisions, naive Bayesian classification, Bayesian decision theory, cost-sensitive classification

• Type: Journal
• Year: 2010
• Where: RSKT
• Authors: Yiyu Yao, Bing Zhou
• Keywords: Bayesian Decision, Information Technology, Naive Bayesian Classifier, Rough Set, RSKT 2010