NMGRS: Neighborhood-based multigranulation rough sets
NMGRS: Neighborhood-based multigranulation rough sets
复制标题
NMGRS：基于邻域的多粒粗糙集
DOI：
10.1016/j.ijar.2012.05.004
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发表时间：
2012-10-01
影响因子：
3.9
通讯作者：
Li, Jinjin
中科院分区：
文献类型：
--
作者：
Lin, Guoping;Qian, Yuhua;Li, Jinjin
最近，多批次粗糙集（MGRS）已成为粗糙集理论的新方向，该理论基于宇宙上的多个二元关系。但是，值得注意的是，原始MGR不能用于从具有各种属性领域的信息系统中发现知识。为了扩展MGR的理论，这项研究的目的是在多层粗糙集的框架中开发出所谓的基于邻里的多批次粗糙集（NMGRS）。此外，通过使用两种不同的近似策略，即寻求共同的保留差异并寻求共同的拒绝差异，我们首先提出了乐观和悲观的1型基于1型的基于社区的多跨度粗糙集，以及乐观和乐观的2型社区基于社区的基于基于社区的多跨度粗糙集，分别。通过分析基于邻里的多层粗糙集的几种重要属性，我们发现新的粗糙集合在邻域的大小等于零时，将新的粗糙集退化为原始MGR。为了在基于邻里的多批次粗糙集中获得覆盖的减排，然后我们提出了一个新的定义，以覆盖还原的新定义，以描述保留邻里决策系统一致性的最小属性子集，可以通过Chen的可见度矩阵方法来计算。这些结果表明，提出的NMGR在很大程度上扩展了经典MGR在多个颗粒的背景下的理论和应用。 （c）2012 Elsevier Inc.保留所有权利。
Recently, a multigranulation rough set (MGRS) has become a new direction in rough set theory, which is based on multiple binary relations on the universe. However, it is worth noticing that the original MGRS can not be used to discover knowledge from information systems with various domains of attributes. In order to extend the theory of MGRS, the objective of this study is to develop a so-called neighborhood-based multigranulation rough set (NMGRS) in the framework of multigranulation rough sets. Furthermore, by using two different approximating strategies, i.e., seeking common reserving difference and seeking common rejecting difference, we first present optimistic and pessimistic 1-type neighborhood-based multigranulation rough sets and optimistic and pessimistic 2-type neighborhood-based multigranulation rough sets, respectively. Through analyzing several important properties of neighborhood-based multigranulation rough sets, we find that the new rough sets degenerate to the original MGRS when the size of neighborhood equals zero. To obtain covering reducts under neighborhood-based multigranulation rough sets, we then propose a new definition of covering reduct to describe the smallest attribute subset that preserves the consistency of the neighborhood decision system, which can be calculated by Chen's discernibility matrix approach. These results show that the proposed NMGRS largely extends the theory and application of classical MGRS in the context of multiple granulations. (C) 2012 Elsevier Inc. All rights reserved.