DOI:

1.安徽大学 网络信息中心;2.安徽三联学院 计算机工程学院

TP181

Incremental Updating Algorithms of Neighborhood Decision-theoretic Rough Set Model for Hybrid Data Based on Matrix Strategy
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1.Anhui University, Network Information Center;2.Anhui Sanlian University，College of Computer Engineering

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摘要:

决策粗糙集模型是当前粗糙集理论最为重要的研究分支之一。然而，由于现实环境下数据类型的复杂多样以及数据的动态更新，使得传统的决策粗糙集模型面临着一定的局限和不足，针对这一问题，提出一种混合型信息系统的邻域决策粗糙集模型，并设计出一种矩阵方法的邻域决策粗糙集增量式更新算法。文中首先将传统的离散型决策粗糙集模型在混合型信息系统下进行推广，提出一种邻域决策粗糙集模型，使得该模型可以直接处理混合型的数据，然后利用矩阵的方法去重新表示了该邻域决策粗糙集模型，同时，针对混合型信息系统对象增加和对象减少时的情形，通过矩阵研究了邻域决策粗糙集模型的增量式更新，理论证明了这种增量式方法的高效性，最后基于矩阵的增量式更新方法，提出了混合型信息系统邻域决策粗糙集的增量式更新算法。实验分析表明所提出的增量式更新算法具有一定的有效性和优越性。

Abstract:

Decision-theoretic rough set model is one of the most important research branches of rough set theory. However, due to the variety of data types and the dynamic updating of data in the real environment, the traditional decision-theoretic rough set model is faced with certain limitations and deficiencies. To solve this problem, a neighborhood decision-theoretic rough set model of hybrid information system is proposed, and an incremental updating algorithm of neighborhood decision-theoretic rough set based on matrix method is designed. In this paper, the traditional discrete decision-theoretic rough set model is extended to the hybrid information system, and a neighborhood decision-theoretic rough set model is proposed, which can deal with the hybrid data directly. Then, the matrix method is used to represent the neighborhood decision-theoretic rough set model. At the same time, the incremental updating of the neighborhood decision-theoretic rough set model is studied through the matrix in the case of the increase and decrease of the objects in the hybrid information system. The efficiency of this incremental method is proved theoretically. Finally, based on the incremental updating method of matrix, the incremental updating algorithm of neighborhood decision-theoretic rough set for hybrid information system is proposed. Experimental results show that the proposed incremental updating algorithm has certain effectiveness and superiority.

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##### 历史
• 收稿日期:2020-10-05
• 最后修改日期:2021-03-30
• 录用日期:2021-04-07
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