AMG-DTRS: Adaptive multi-granulation decision-theoretic rough sets

The Multi-Granulation Decision-Theoretic Rough Set (MG-DTRS) is an effective method for cost-sensitive decision making from multi-view and multi-level. However, the inherent weak point of MG-DTRS model is to compute three regions with a subjectively given pair of probabilistic parameters (i.e., alpha and beta). To overcome this issue, this paper first proposes a generalized MG-DTRS model called Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS), which can adaptively obtain a pair of probabilistic thresholds by setting a compensation coefficient zeta. Then, three types of mean AMG-DTRS models are investigated, which provide a novel perspective on multi-granulation method for information fusion. Finally, the connections and differences between the proposed AMG-DTRS and the existing MGRS models are analyzed, which show the advantages and generalization of the AMG-DTRS model. In addition, there are numerous existing MGRS models can be derived explicitly by considering various MG-DTRS, MGRS and VP-MGRS based on our model. These results will be conducive to establishing the framework of information fusion for granular computing. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces an Adaptive Multi-Granulation Decision-Theoretic Rough Sets (AMG-DTRS) model, which adaptively obtains probabilistic thresholds by setting a compensation coefficient. Three types of mean AMG-DTRS models are studied, offering a new perspective on information fusion. The advantages and generalization of the AMG-DTRS model are demonstrated by analyzing its connections and differences with existing MGRS models.