2016Unpublished venueRequires access

On multi-granularity soft rough sets

Xiaomin Wang, Ying Liu, Piyu Li, Jianbo Liu

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Abstract

In this paper, we present a possible fusion of rough sets and multiple soft sets. According to the theory of soft rough sets we propose the concepts of the multi-granularity soft rough sets (MGSR-sets) and multi-granularity soft approximation space. Based on these, we consider the multi-granularity soft rough approximation operators and discuss some important properties of them by some illustrative examples. Finally, the jointly full soft set and multi-granularity soft rough relations are also introduced.

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What this paper is about

In this paper, we present a possible fusion of rough sets and multiple soft sets. According to the theory of soft rough sets we propose the concepts of the multi-granularity soft rough sets (MGSR-sets) and multi-granularity soft approximation space. Based on these, we consider the multi-granularity soft rough approximation operators and discuss some important properties of them by some illustrative examples. Finally, the jointly full soft set and multi-granularity soft rough relations are also introduced.

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Available abstract

In this paper, we present a possible fusion of rough sets and multiple soft sets. According to the theory of soft rough sets we propose the concepts of the multi-granularity soft rough sets (MGSR-sets) and multi-granularity soft approximation space. Based on these, we consider the multi-granularity soft rough approximation operators and discuss some important properties of them by some illustrative examples. Finally, the jointly full soft set and multi-granularity soft rough relations are also introduced.

Key concepts: Granularity, Rough set, Computer science, Granular computing, Soft set, Set (abstract data type), Dominance-based rough set approach, Soft computing

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