Abstract
Recently, various types of single valued neutrosophic (SVN) rough set models were presented based on the same inclusion relation. However, there is another SVN inclusion relation in SVN sets. In this paper, we propose a new type of SVN covering rough set model based on the new inclusion relation. Furthermore, the graph and matrix representations of the new SVN covering approximation operators are presented. Firstly, the notion of SVN β 2 -covering approximation space is proposed, which is decided by the new inclusion relation. Then, a type of SVN covering rough set model under the SVN β 2 -covering approximation space is presented. Moreover, there is a corresponding SVN relation rough set model based on a SVN relation induced by the SVN β 2 -covering, and two conditions under which the SVN β 2 -covering can induce a symmetric SVN relation are presented. Thirdly, the graph and matrix representations of the new SVN covering rough set model are investigated. Finally, we propose a novel method for decision making (DM) problems in paper defect diagnosis under the new SVN covering rough set model.
Highlights
Rough set theory, as a tool to deal with various types of data in data mining, was proposed by Pawlak [1,2] in 1982
We present some new concepts in single valued neutrosophic (SVN) β2 -covering approximation space, as well as their properties
We present a method to decision making (DM) problems in paper defect diagnosis, which is an important topic in paper making industries, under the type-2 SVN covering rough set model
Summary
As a tool to deal with various types of data in data mining, was proposed by Pawlak [1,2] in 1982. The investigation of the SVN β2 -covering approximation space and its corresponding SVN covering rough set model is very important. It can manage some issues that the SVN β-covering approximation space can not deal with, and constructs a new type of SVN covering rough set model This is our motivation of this research. The type-2 SVN covering rough set model under the SVN β2 -covering approximation space is proposed. We present a method to DM problems in paper defect diagnosis, which is an important topic in paper making industries, under the type-2 SVN covering rough set model.
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