Integrating of rough sets and vague soft sets theory in covering approximation space

Proposed in this paper is a novel covering rough Vague soft sets model based on neighbor domain and some related properties, which is applied to the problem of integrating rough sets,Vague sets and soft sets theory in covering approximation space. At the same time, a new uncertainty measurement method is defined, which is applied to the problem of uncertainty measurement for covering rough Vague soft sets. In this method, first, the uncertainty of covering granular space based on knowledge entropy in covering granular space is defined. Next, the roughness for covering rough Vague soft sets itself is defined based on roughness. Then, the knowledge entropy in covering granular space and roughness for covering rough Vague soft sets are combinatively used to measure the uncertainty degree for covering rough Vague soft sets model. Experimental results show that this method is effective and practical, and the uncertainty degree for the covering rough Vague soft sets increase with the increase of the covering granularity. Experimental results show that the covering rough Vague soft sets has good application prospect in EGG analysis and the auxiliary diagnosis problem.