Abstract
Multigranulation decision-theoretic rough sets (MDTRS) is a model for real-world decision making. In various existing optimistic MDTRS models, the lower and upper approximations are defined based on the strategy seeking commonality while preserving differences, while pessimistic MDTRS models based on the strategy Seeking commonality while eliminating differences in the definitions of approximations. In real-world problems, one may need different strategies in defining lower approximations and upper approximations. This paper proposes two new MDTRS approaches in the frameworks of multi-covering approximation spaces by using different strategy in defining lower and upper approximations, namely, covering-based optimistic-pessimistic multigranulation decision-theoretic rough sets and covering-based pessimistic-optimistic multigranulation decision-theoretic rough sets, respectively. We explore a number of basic properties of the proposed models. Then, we elaborate on the relationship between the proposed models and the existing ones in the literature. And we also disclose the interrelationships of the proposed models. Finally, we provide a case study to demonstrate the effectiveness of the proposed models.