Rough Vague Sets and its Knowledge Acquisition

Abstract

The rough sets approach to vague concept approximation is an emerging area of intelligence information processing that offers new theories, techniques, and tools for analysis of vague data sets. This paper makes an attempt to extend the traditional rough sets approaches to the vague data environment. First, we defined the logical implication relationship between two vague values. The implication operator is used to define vague measures between two vague sets containment. Then, by using the containment and intersection between vague sets, the notions of the lower and upper generalized rough vague approximations of a vague set are introduced. Finally, with the rough vague lower and upper approximations, we developed theories and approaches for attribute reduction and knowledge acquisition in vague objective information systems. The results of this paper show that our studies have extended the corresponding methods in classical rough sets theory, and provided a new avenue for uncertain knowledge acquisition in vague information systems.