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A New Approach to Establish Variable Consistency Dominance-Based Rough Sets Based on Dominance Matrices

机译:基于优势矩阵建立基于变量一致性优势的粗糙集的新方法

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摘要

Rough set theory is an effective mathematical tool for dealing with inconsistencies in information systems. Dominance based rough set (DBRS) is an extension to the original rough set, in which the equivalence relation is replaced by a dominance relation. However, in some condition, the lower approximation of DBRS can be emptied by only one “malicious” object. The variable consistency dominance based rough set (VC-DBRS) is proposed to avoid situations like this. The core conception of VC-DBRS is introducing parameters to control the consistency of objects including in lower approximations. There are several kinds of consistency measures have been proposed, but it is difficult to compute them by manual, especially for a large data set. It is necessary to find out an approach to calculate these measures automatically. This paper proposes a new algorithm based on dominance matrices to calculate the rough membership for VC-DBRS, and then presents how to use this measure to get the lower and upper approximation.
机译:粗糙集理论是一种有效的数学工具,可以解决信息系统中的不一致问题。基于优势的粗糙集(DBRS)是对原始粗糙集的扩展,其中等价关系被优势关系代替。但是,在某些情况下,只能通过一个“恶意”对象清空DBRS的较低近似值。提出了基于可变一致性优势的粗糙集(VC-DBRS)来避免这种情况。 VC-DBRS的核心概念是引入参数来控制对象的一致性,包括较低的近似值。已经提出了几种一致性度量,但是很难手动计算它们,尤其是对于大型数据集。有必要找到一种自动计算这些度量的方法。本文提出了一种基于优势矩阵的新算法来计算VC-DBRS的粗糙隶属度,然后提出了如何使用该度量获得上下近似。

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