Approach to decision information fusion based on improved Bayesian rough set and evidence theory
K Chen · 2014
According to the classification problem of the new object in the decision information table,this paper constructed a method of decision information fusion based on the theory of Bayesian rough set and evidence theory. To deal with the problem of multiple decision classes,it improved the traditional Bayesian rough set theory. It defined the support degree to express the exact classification. Then it calculated the approximation classified quality and the certainty gain function of the Bayesian rough set to rate the support degree of the evidences,and used the normalization method to construct the basic probability assignments. It fused the evidences by using the D-S combination rule. Finally,it applied the proposed method to the problems of equipment fault diagnosis,and the results show the effectiveness of practical application of this method.