Soft set theory—First results
TLDR
The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the the theory, and to discuss some problems of the future.Abstract:
The soft set theory offers a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. The main purpose of this paper is to introduce the basic notions of the theory of soft sets, to present the first results of the theory, and to discuss some problems of the future.read more
Citations
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On algebraic structure of intuitionistic fuzzy soft sets
TL;DR: This paper further discusses the operation properties and algebraic structure of intuitionistic fuzzy soft sets, and introduces the notions of (@c,@d)-intuitionistic fuzzysoft equalities and soft equalities introduced by Qin and Hong.
Journal ArticleDOI
Linear Diophantine Fuzzy Relations and Their Algebraic Properties with Decision Making
TL;DR: In this article, a robust fusion of binary relations and linear Diophantine fuzzy sets (LDFSs) is proposed to model vagueness and uncertainty in decision-making problems, where the use of reference parameters corresponding to the membership and non-membership fuzzy relations makes it most accommodating towards modeling uncertainties in real-life problems.
On intuitionistic fuzzy soft topological spaces
Sadi Bayramov,Cigdem Gunduz +1 more
TL;DR: Some important properties of intuitionistic fuzzy soft topological spaces and deflne the intuitionism fuzzy soft closure and interior of an intuitionists fuzzy soft set are introduced.
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FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory
TL;DR: It is shown that the proposed Fuzzy Soft Set Classifier FSSC provides better accuracy and higher accuracy as compared to the baseline algorithm using soft set theory.
Journal ArticleDOI
An Alternative Approach to Normal Parameter Reduction Algorithm for Soft Set Theory
TL;DR: It is shown that the proposed algorithm can reduce the computational complexity and run time compared with baseline algorithms, and is relatively easy to understand compared with the state of the art of normal parameter reduction algorithm.
References
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