Attribute Reduction in Bayesian Rough Set Model for Binary Decision Problems
二值决策Bayesian粗糙集模型属性约简研究

Bayesian rough set model(BRSM) , as the hybrid development between rough set theory and I3ayesian reasoning,can deal with many practical problems which could not be effectively handled by Pawlak's rough set model. The prior probabilities of events are considered as a benchmark when determining the approximation regions in BRSM. In this paper, the equivalence between two kinds of current attribute reduction models in BRSM for binary decision problems,viz. Slezak and Ziarko's modcls,was proved. Furthermore, an associated discernibility matrix for binary decision problems in BRSM was proposed, with which the available attribute reduction methods based on discernibility matrices in the Pawlak's rough set model can be transferred to BRSM.