Graded rough set model based on two universes and its properties
Graded rough set model based on two universes and its properties
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DOI：
10.1016/j.knosys.2012.02.012
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发表时间：
2012-09
期刊：
影响因子：
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通讯作者：
Caihui Liu;Duoqian Miao;N. Zhang
中科院分区：
文献类型：
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作者：
Caihui Liu;Duoqian Miao;N. Zhang
近年来，基于两论域的粗糙集模型受到了广泛的关注，人们从不同的角度提出了不同的两论域粗糙集模型。本文提出了一种新的模型，即，从绝对定量的角度提出了两个不同但相关论域上的分级粗糙集模型（GRSTU）。研究了GRSTU中近似算子的基本性质，并提出了一种基于关系矩阵的算法来计算GRSTU中对象集合的上下近似。进一步讨论了经典粗糙集模型与GRSTU之间的关系，并给出了与GRSTU相关的一些结论。最后，通过几个实例说明了GRSTU的概念论证，并详细说明了GRSTU的应用。
In recent years, much attention has been given to the rough set models based on two universes of discourse and different kinds of rough set models on two universes have been developed from different points of view. In this paper, a novel model, i.e., the graded rough set model on two distinct but related universes (GRSTU) is proposed from the absolute quantitative point of view. We study the basic properties of approximation operators in GRSTU, and introduce a relation matrix based algorithm to compute the lower and upper approximations of a set of objects in GRSTU. Furthermore, the relationships between classical rough set model and GRSTU are discussed and some conclusions related to the GRSTU are given. Finally, several examples are employed to demonstrate the conceptual arguments of GRSTU, and an application of GRSTU is also illuminated in details.