A Variable Precision Covering-Based Rough Set Model Based on Functions

الملخص EN

Classical rough set theory is a technique of granular computing for handling the uncertainty, vagueness, and granularity in information systems.

Covering-based rough sets are proposed to generalize this theory for dealing with covering data.

By introducing a concept of misclassification rate functions, an extended variable precision covering-based rough set model is proposed in this paper.

In addition, we define the f-lower and f-upper approximations in terms of neighborhoods in the extended model and study their properties.

Particularly, two coverings with the same reductions are proved to generate the same f-lower and f-upper approximations.

Finally, we discuss the relationships between the new model and some other variable precision rough set models.