Abstract
In this paper, to combine single valued neutrosophic sets (SVNSs) with covering-based rough sets, we propose two types of single valued neutrosophic (SVN) covering rough set models. Furthermore, a corresponding application to the problem of decision making is presented. Firstly, the notion of SVN β -covering approximation space is proposed, and some concepts and properties in it are investigated. Secondly, based on SVN β -covering approximation spaces, two types of SVN covering rough set models are proposed. Then, some properties and the matrix representations of the newly defined SVN covering approximation operators are investigated. Finally, we propose a novel method to decision making (DM) problems based on one of the SVN covering rough set models. Moreover, the proposed DM method is compared with other methods in an example.
Highlights
Rough set theory, as a a tool to deal with various types of data in data mining, was proposed by Pawlak [1,2] in 1982
We present two types of single valued neutrosophic (SVN) covering rough set models
We propose the other SVN covering rough set model, which concerns the crisp lower and upper approximations of each crisp set in the SVN environment
Summary
As a a tool to deal with various types of data in data mining, was proposed by Pawlak [1,2] in 1982. Covering-based rough sets [3,4,5] were proposed to deal with the type of covering data In application, they have been applied to knowledge reduction [6,7], decision rule synthesis [8,9], and other fields [10,11,12]. Zadeh’s fuzzy set theory [23] addresses the problem of how to understand and manipulate imperfect knowledge. It has been used in various applications [24,25,26,27]. There are many fuzzy covering rough set models proposed by researchers, such as Ma [28] and Yang et al [20]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
