Multi-granularity decision-theoretic rough sets based on the fuzzy T-equivalence relation with new strategies

Abstract

Fuzzy rough sets and multi-granularity rough sets are essential extensions of Pawlak rough sets, which have become artificial intelligence research hotspots. Previous studies of the rough sets based on the fuzzy T-equivalence relation did not take the multi-granularity into account. The multi-granularity data is typically the multi-view cognition obtained by different granularity of the data, and its distinctive feature is that the data can be presented in different granularity spaces. In this paper, we integrate the idea of multi-granularity and propose four new models of “optimistic,” “pessimistic,” “optimistic-pessimistic,” and “pessimistic-optimistic” decision-theoretic rough sets based on the fuzzy T-equivalence relation for the first time, followed by a preliminary analysis of the intrinsic relations and properties of these new decision-theoretic rough set models by a concrete example. At last, we use experiments to show the effectiveness of suggested models, proving that they are both rational and practical.