Chang-xing Li

Bio: Chang-xing Li is an academic researcher. The author has contributed to research in topics: Soft set & Rough set. The author has an hindex of 2, co-authored 2 publications receiving 531 citations.

Soft sets combined with fuzzy sets and rough sets: a tentative approach

Abstract: Theories of fuzzy sets and rough sets are powerful mathematical tools for modelling various types of uncertainty. Dubois and Prade investigated the problem of combining fuzzy sets with rough sets. Soft set theory was proposed by Molodtsov as a general framework for reasoning about vague concepts. The present paper is devoted to a possible fusion of these distinct but closely related soft computing approaches. Based on a Pawlak approximation space, the approximation of a soft set is proposed to obtain a hybrid model called rough soft sets. Alternatively, a soft set instead of an equivalence relation can be used to granulate the universe. This leads to a deviation of Pawlak approximation space called a soft approximation space, in which soft rough approximations and soft rough sets can be introduced accordingly. Furthermore, we also consider approximation of a fuzzy set in a soft approximation space, and initiate a concept called soft---rough fuzzy sets, which extends Dubois and Prade's rough fuzzy sets. Further research will be needed to establish whether the notions put forth in this paper may lead to a fruitful theory.

Soft Set Based Approximate Reasoning: A Quantitative Logic Approach

Abstract: Soft set theory is a newly emerging mathematical approach to vagueness. However, it seems that there is no existing research devoted to the discussion of applying soft sets to approximate reasoning. This paper aims to initiate an approximate reasoning scheme based on soft set theory. We consider proposition logic in the framework of a given soft set. By taking parameters of the underlying soft set as atomic formulas, the concept of (well-formed) formulas over a soft set is defined in a natural way. The semantic meaning of formulas is then given by taking objects of the underlying soft set as valuation functions. We propose the notion of decision soft sets and define decision rules as implicative type of formulas in decision soft sets. Motivated by basic ideas from quantitative logic, we also introduce several measures and preorders to evaluate the soundness of formulas and decision rules in soft sets. Moreover, an interesting example is presented to illustrate all the new concepts and the basic ideas initiated here.

Neutrosophic Soft Set

Abstract: In this paper we study the concept of neutrosophic set of Smarandache. We have introduced this concept in soft sets and de¯ned neutrosophic soft set. Some de¯nitions and operations have been intro- duced on neutrosophic soft set. Some properties of this concept have been established.

Interval-valued intuitionistic fuzzy soft sets and their properties

Abstract: Molodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. However, it has been pointed out that classical soft sets are not appropriate to deal with imprecise and fuzzy parameters. In this paper, the notion of the interval-valued intuitionistic fuzzy soft set theory is proposed. Our interval-valued intuitionistic fuzzy soft set theory is a combination of an interval-valued intuitionistic fuzzy set theory and a soft set theory. In other words, our interval-valued intuitionistic fuzzy soft set theory is an interval-valued fuzzy extension of the intuitionistic fuzzy soft set theory or an intuitionistic fuzzy extension of the interval-valued fuzzy soft set theory. The complement, ''and'', ''or'', union, intersection, necessity and possibility operations are defined on the interval-valued intuitionistic fuzzy soft sets. The basic properties of the interval-valued intuitionistic fuzzy soft sets are also presented and discussed.

An adjustable approach to intuitionistic fuzzy soft sets based decision making

Abstract: Molodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. There has been some progress concerning practical applications of soft set theory, especially the use of soft sets in decision making. In this paper we generalize the adjustable approach to fuzzy soft sets based decision making. Concretely, we present an adjustable approach to intuitionistic fuzzy soft sets based decision making by using level soft sets of intuitionistic fuzzy soft sets and give some illustrative examples. The properties of level soft sets are presented and discussed. Moreover, we also introduce the weighted intuitionistic fuzzy soft sets and investigate its application to decision making.

Intuitionistic fuzzy parameterized soft set theory and its decision making

Abstract: HighlightsWe define a intuitionistic fuzzy parameterized soft sets for dealing with uncertainties that is based on both soft sets and intuitionistic fuzzy sets.We investigated their operations and properties.We introduce a decision making method based on intuitionistic FP-soft sets. In this work, we first define intuitionistic fuzzy parameterized soft sets (intuitionistic FP-soft sets) and study some of their properties. We then introduce an adjustable approaches to intuitionistic FP-soft sets based decision making. Finally, we give a numerical example which shows that this method successfully works.

A note on soft topological spaces

Abstract: Shabir and Naz (2011) [12] introduced and studied the notions of soft topological spaces, soft interior, soft closure and soft separation axioms. But we found that some results are incorrect (see their Remark 3.23). So the purpose of this note is, first, to point out some errors in Remark 4 and Example 9 of Shabir and Naz (2011) [12], and second, to investigate properties of soft separation axioms defined in Shabir and Naz (2011) [12]. In particular, we investigate the soft regular spaces and some properties of them. We show that if a soft topological space (X,@t,E) is soft T"1 and soft regular (i.e. a soft T"3-space), then (x,E) is soft closed for each [email protected]?X (their Theorem 3.21).