Uncertainty Improvement of Incomplete Decision System using Bayesian Conditional Information Entropy

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.