Approximation of Classification and Measures of Uncertainty in Rough Set on Two Universal Sets
This work addresses a domain-specific extension in rough set theory for handling relational data between different universes, representing an incremental advancement.
The paper tackles the problem of extending rough set theory from a single universal set to two universal sets to better model real-world information systems, and introduces approximation of classifications and measures of uncertainty based on binary relations.
The notion of rough set captures indiscernibility of elements in a set. But, in many real life situations, an information system establishes the relation between different universes. This gave the extension of rough set on single universal set to rough set on two universal sets. In this paper, we introduce approximation of classifications and measures of uncertainty basing upon rough set on two universal sets employing the knowledge due to binary relations.