We show that many local MGRS models can be derived from the LMG-DTRS framework. Furthermore, we present two types of local MGRS frameworks under PRSs and variable precision rough sets, where the relationships between them and the LMG-DTRS model are also discussed. In addition, we verify the efficiency of a concept approximation algorithm designed with LMG-DTRS based on theoretical and experimental analyses.
We also explore a number of important properties of LMG-DTRSs. In this study, to address these issues, we propose the combination of local rough sets with multigranulation decision-theoretic rough sets to obtain local multigranulation decision-theoretic rough sets (LMG-DTRSs) as a semi-unsupervised learning method. However, in the era of big data, labeling all data is almost infeasible in some cases. Second, MGRSs comprise a supervised learning method, so they often require a large amount of labeled data. First, similar to other rough set models, calculating the approximation of a target set is extremely time-consuming for larger scale data. Multigranulation rough sets (MGRSs) where a target concept is approximated by granular structures induced by multiple binary relations have been applied successfully in many domains but they are still affected by two issues.
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