New Topological Approaches to Generalized Soft Rough Approximations with Medical Applications

There are many approaches to deal with vagueness and ambiguity including soft sets and rough sets. Feng et al. initiated the concept of possible hybridization of soft sets and rough sets. They introduced the concept of soft rough sets, in which parameterized subsets of a universe set serve as the building blocks for lower and upper approximations of a subset. Topological notions play a vital role in rough sets and soft rough sets. So, the basic objectives of the current work are as follows: first, we find answers to some very important questions, such as how to determine the probability that a subset of the universe is definable. Some more similar questions are answered in rough sets and their extensions. Secondly, we enhance soft rough sets from topological perspective and introduce topological soft rough sets. We explore some of their properties to improve existing techniques. A comparison has been made with some existing studies to show that accuracy measure of proposed technique shows an improvement. Proposed technique has been employed in decision-making problem for diagnosing heart failure. For this two algorithms have been given.

A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets

The data we need to deal with is getting bigger and bigger in recent years, and the same happens to multi-granulation rough set, so updated schemes have been proposed with the variation of attributes or attribute values in multi-granulation rough sets, this paper puts forward a dynamic mechanism to update the approximations of multi-granulation rough sets when adding or deleting objects. Firstly, the relationships between the original approximations and updated approximations are explored when adding or deleting objects in multi-granulation rough sets, and the dynamic processes of updating optimistic and pessimistic multi-granulation rough approximations are proposed. Secondly, two corresponding dynamic algorithms are proposed to update the lower and upper approximations of optimistic and pessimistic multi-granulation rough sets. Finally, a great quantity of experiments had been implemented, and the results indicate that two dynamic algorithms proposed are more effective than the static algorithm.

Applications of Rough Soft to Extensions Semihypergroups Induced by Operators and Corresponding Decision Making Methods

In this article, we apply rough soft set to a special algebraic hyperstructure, which obtained of disjoint Γ-semihypergroups, and give the concept of rough soft semihypergroup. We propose the notion of lower and upper approximations with respect to a special semihypergroup and obtain some properties of them. Moreover, we consider a connection between lower(upper) approximation of a special semihypergroup and lower(upper) approximation of associated Γ-hypergroupoid. In the last section of this research, we discuss the decision making algorithm of rough soft semihypergroups and we obtain a relation between the decision making algorithm of rough soft semihypergroups and their associated rough soft Γ-hypergroupoid for a special semihypergroup.

Topological Structures of Lower and Upper Rough Subsets in a Hyperring

In this paper, we study the connection between topological spaces, hyperrings (semi-hypergroups), and rough sets. We concentrate here on the topological parts of the lower and upper approximations of hyperideals in hyperrings and semi-hypergroups. We provide the conditions for the boundary of hyp-ideals of a hyp-ring to become the hyp-ideals of hyp-ring.

Certain types of fuzzy soft β-covering based fuzzy rough sets with application to decision-making

The aim of this paper is to introduce and study different kinds of fuzzy soft β-neighborhoods called fuzzy soft β-adhesion neighborhoods and to analyze some of their properties. Further, the concepts of soft β-adhesion neighborhoods are investigated and the related properties are studied. Then, we present new kinds of lower and upper approximations by means of different fuzzy soft β-neighborhoods. The relationships among our models (i.e., Definitions 3.9, 3.12, 3.15 and 3.18) and Zhang models [48] are also discussed. Finally, we construct an algorithm based on Definition 3.12, when k = 1 to solve the decision-making problems and illustrate its applicability through a numerical example.

Multigranulation roughness based on soft relations

The original rough set model, developed by Pawlak depends on a single equivalence relation. Qian et al, extended this model and defined multigranulation rough sets by using finite number of equivalence relations. This model provide new direction to the research. Recently, Shabir et al. proposed a rough set model which depends on a soft relation from an universe V to an universe W . In this paper we are present multigranulation roughness based on soft relations. Firstly we approximate a non-empty subset with respect to aftersets and foresets of finite number of soft binary relations. In this way we get two sets of soft sets called the lower approximation and upper approximation with respect to aftersets and with respect to foresets. Then we investigate some properties of lower and upper approximations of the new multigranulation rough set model. It can be found that the Pawlak rough set model, Qian et al. multigranulation rough set model, Shabir et al. rough set model are special cases of this new multigranulation rough set model. Finally, we added two examples to illustrate this multigranulation rough set model.

Topological Approaches for Rough Continuous Functions with Applications

In this paper, we purposed further study on rough functions and introduced some concepts based on it. We introduced and investigated the concepts of topological lower and upper approximations of near-open sets and studied their basic properties. We defined and studied new topological neighborhood approach of rough functions. We generalized rough functions to topological rough continuous functions by different topological structures. In addition, topological approximations of a function as a relation were defined and studied. Finally, we applied our approach of rough functions in finding the images of patient classification data using rough continuous functions.

Approximations of pythagorean fuzzy sets over dual universes by soft binary relations

Yager introduced the Pythagorean Fuzzy Set (PFS) to deal with uncertainty in real-world decision-making problems. Binary relations play an important role in mathematics as well as in information sciences. Soft binary relations give us a parameterized collection of binary relations. In this paper, lower and upper approximations of PFSs based on Soft binary relations are given with respect to the aftersets and with respect to the foresets. Further, two kinds of Pythagorean Fuzzy Topologies induced by Soft reflexive relations are investigated and an accuracy measure of a PFS is provided. Besides, based on the score function and these approximations of PFSs, an algorithm is constructed for ranking and selection of the decision-making alternatives. Although many MCDM (multiple criteria decision making) methods for PFSs have been proposed in previous studies, some of those cannot solve when a person is encountered with a two-sided matching MCDM problem. The proposed method is new in the literature. This newly proposed model solved the problem more accurately. The proposed method focuses on selecting and ranking from a set of feasible alternatives depending on the two-sided matching of attributes and determines a ranking based solution for a problem with conflicting criteria to help the decision-maker in reaching a final course of action.

Bi-covering rough sets

Rough set theory is a useful tool for knowledge discovery and data mining. Covering-based rough sets are important generalizations of the classical rough sets. Recently, the concept of the neighborhood has been applied to define different types of covering rough sets. In this paper, based on the notion of bi-neighborhood, four types of bi-neighborhoods related bi-covering rough sets were defined with their properties being discussed. We first show some basic properties of the introduced bi-neighborhoods. We then explore the relationships between the considered bi-covering rough sets and investigate the properties of them. Also, we show that new notions may be viewed as a generalization of the previous studies covering rough sets. Finally, figures are presented to show that the collection of all lower and upper approximations (bi-neighborhoods of all elements in the universe) introduced in this paper construct a lattice in terms of the inclusion relation ?.

Rough sets theory via new topological notions based on ideals and applications

<abstract><p>There is a close analogy and similarity between topology and rough set theory. As, the leading idea of this theory is depended on two approximations, namely lower and upper approximations, which correspond to the interior and closure operators in topology, respectively. So, the joined study of this theory and topology becomes fundamental. This theory mainly propose to enlarge the lower approximations by adding new elements to it, which is an equivalent goal for canceling elements from the upper approximations. For this intention, one of the primary motivation of this paper is the desire of improving the accuracy measure and reducing the boundary region. This aim can be achieved easily by utilizing ideal in the construction of the approximations as it plays an important role in removing the vagueness of concept. The emergence of ideal in this theory leads to increase the lower approximations and decrease the upper approximations. Consequently, it minimizes the boundary and makes the accuracy higher than the previous. Therefore, this work expresses the set of approximations by using new topological notions relies on ideals namely $ \mathcal{I} $-$ {\delta_{\beta}}_{J} $-open sets and $ \mathcal{I} $-$ {\bigwedge_{\beta}}_{J} $-sets. Moreover, these notions are also utilized to extend the definitions of the rough membership relations and functions. The essential properties of the suggested approximations, relations and functions are studied. Comparisons between the current and previous studies are presented and turned out to be more precise and general. The brilliant idea of these results is increased in importance by applying it in the chemical field as it is shown in the end of this paper. Additionally, a practical example induced from an information system is introduced to elucidate that the current rough membership functions is better than the former ones in the other studies.</p></abstract>