title: A variable precision covering-based rough set model based on functions.
abstract: 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.
authors.affiliation: Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Lab of Granular Computing, Minnan Normal University, Zhangzhou 363000, China.
authors.forename: William
authors.identifier: 0000-0001-8898-9244
authors.initials: W
authors.lastname: Zhu
mesh_terms: D008962:Models, Theoretical; D011336:Probability