scholarly journals Picture Fuzzy Rough Set and Rough Picture Fuzzy Set on Two Different Universes and Their Applications

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Dliouah Ahmed ◽  
Binxiang Dai
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Fuzzy Relation ◽  
Type I ◽  
Type Ii ◽  
Fuzzy Rough Set ◽  
Fuzzy Approximation ◽  
Picture Fuzzy Set ◽  
Decision Making Problem ◽  
Picture Fuzzy Sets

The major concern of this article is to propose the notion of picture fuzzy rough sets (PFRSs) over two different universes which depend on δ , ζ , ϑ -cut of picture fuzzy relation ℛ on two different universes (i.e., by combining picture fuzzy sets (PFSs) with rough sets (RSs)). Then, we discuss several interesting properties and related results on the PFRSs. Furthermore, we define some notions related to PFRSs such as (Type-I/Type-II) graded PFRSs, the degree α and β with respect to ℛ δ , ζ , ϑ on PFRSs, and (Type-I/Type-II) generalized PFRSs based on the degree α and β with respect to ℛ δ , ζ , ϑ and investigate the basic properties of above notions. Finally, an approach based on the rough picture fuzzy approximation operators on two different universes in decision-making problem is introduced, and we give an example to show the validity of this approach.

Logarithmic Entropy Measures for Fuzzy Rough Set and their Application in Decision Making Problem

2020 ◽  
Vol 9 (2) ◽  
pp. 80-97
Author(s):  
Omdutt Sharma ◽  
Priti Gupta
Keyword(s):  
Decision Making ◽  
Information Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Critical Problem ◽  
Fuzzy Rough Set ◽  
Probabilistic Measure ◽  
Entropy Measures ◽  
Decision Making Problem ◽  
Probabilistic Entropy

Decision-making is a critical problem in various circumstances where some vagueness and ambiguity is found in information. To handle these types of problems, entropy is an important measure of information theory which is exploited to evaluate the uncertain degree of any data. There are two methodologies to determine the entropy, one is probabilistic in nature and other is non-probabilistic. It is shown that for every probabilistic measure there is a corresponding non-probabilistic measure. In this article, some logarithmic non-probabilistic entropy measures have been proposed for the fuzzy rough set corresponding to existing probabilistic entropy measures. The proposed measures are employed in a decision-making problem, which is related to the agriculture. Finally, these proposed measures are compared with the existing trigonometric entropy measures for fuzzy rough sets.

scholarly journals Further Study of MultigranulationT-Fuzzy Rough Sets

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Wentao Li ◽  
Xiaoyan Zhang ◽  
Wenxin Sun
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Approximation Space ◽  
Fuzzy Rough Sets ◽  
Fuzzy Rough Set ◽  
Upper Approximation ◽  
Fuzzy Approximation ◽  
Approximation Operators ◽  
Special Instance

The optimistic multigranulationT-fuzzy rough set model was established based on multiple granulations underT-fuzzy approximation space by Xu et al., 2012. From the reference, a natural idea is to consider pessimistic multigranulation model inT-fuzzy approximation space. So, in this paper, the main objective is to make further studies according to Xu et al., 2012. The optimistic multigranulationT-fuzzy rough set model is improved deeply by investigating some further properties. And a complete multigranulationT-fuzzy rough set model is constituted by addressing the pessimistic multigranulationT-fuzzy rough set. The full important properties of multigranulationT-fuzzy lower and upper approximation operators are also presented. Moreover, relationships between multigranulation and classicalT-fuzzy rough sets have been studied carefully. From the relationships, we can find that theT-fuzzy rough set model is a special instance of the two new types of models. In order to interpret and illustrate optimistic and pessimistic multigranulationT-fuzzy rough set models, a case is considered, which is helpful for applying these theories to practical issues.

Rough Approximations on Hesitant Fuzzy Sets

2016 ◽  
pp. 265-279
Author(s):  
D. Deepak ◽  
Sunil Jacob John
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Fuzzy Relation ◽  
Equivalence Relations ◽  
Hesitant Fuzzy Set ◽  
Fuzzy Rough Sets ◽  
Fuzzy Rough Set ◽  
Dual Nature ◽  
Fuzzy Environment ◽  
Lower And Upper Approximations

Introduction of hesitant fuzzy rough sets would facilitate the use of rough set based techniques to hesitant fuzzy environment. Hesitant fuzzy rough sets deal with the lower and upper approximations in a hesitant fuzzy domain. For this purpose concepts of hesitant fuzzy relations are discussed first to create a theoretical framework to study hesitant fuzzy rough sets. The concepts of equivalence relations are discussed. Hesitant fuzzy rough sets and the properties of the approximations are discussed. The dual nature of the lower and upper approximations is proved. This chapter introduces the model of a hesitant fuzzy rough set which approximates a hesitant fuzzy set using a hesitant fuzzy relation.

scholarly journals Graded Rough Sets based on Neighborhood Operator Over two Different Universes and its Application

2021 ◽  
Author(s):  
Ahmed Mostafa Khalil
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Sets ◽  
The Other ◽  
Type I ◽  
Type Ii ◽  
Type Iii ◽  
Other Hand ◽  
Decision Methods ◽  
Neighborhood Rough Sets

Abstract Abstract The major concern of this paper is to present the notion of rough set based on neighborhood operator on universe set, along with its properties, and examples. Then, we generalize several notions of covering rough sets to neighborhood rough sets with respect to the graded n. Further, we present some notions such as probabilistic neighborhood rough approximations of X, (Type-I / Type-II) probabilistic neighborhood rough approximations of X with error α and β, and (Type-I / Type-II) probabilistic neighborhood rough approximations of X with respect to N . The interesting properties of above notions are investigated in detail. On the other hand, we define the notion of rough set based on neighborhood operator over two different universes. Subsequently, we present some notions (Type-I / Type-II / Type-III) graded n-neighborhood rough sets and give a two approaches to decision-making problems based on the (Type-II / Type-III) grade n-neighborhood rough sets. Then, we construct the decision steps and give two algorithms of the decision methods. Also, we will give two illustrative examples to show the applicability of the rough set based on neighborhood operator over two different universes to solve the rough decision-making problems. Finally, we give a comparison between the Liu et al.’s approach and our approach.

GENERALIZED FUZZY ROUGH SETS BY CONDITIONAL PROBABILITY RELATIONS

2002 ◽  
Vol 16 (07) ◽  
pp. 865-881 ◽  
Author(s):  
ROLLY INTAN ◽  
MASAO MUKAIDONO
Keyword(s):  
Conditional Probability ◽  
Rough Set ◽  
Real World ◽  
Rough Sets ◽  
Similarity Relation ◽  
Fuzzy Rough Set ◽  
Fuzzy Similarity ◽  
Fuzzy Similarity Relation ◽  
Real World Applications ◽  
The Universe

In 1982, Pawlak proposed the concept of rough sets with a practical purpose of representing indiscernibility of elements or objects in the presence of information systems. Even if it is easy to analyze, the rough set theory built on a partition induced by equivalence relation may not provide a realistic view of relationships between elements in real-world applications. Here, coverings of, or nonequivalence relations on, the universe can be considered to represent a more realistic model instead of a partition in which a generalized model of rough sets was proposed. In this paper, first a weak fuzzy similarity relation is introduced as a more realistic relation in representing the relationship between two elements of data in real-world applications. Fuzzy conditional probability relation is considered as a concrete example of the weak fuzzy similarity relation. Coverings of the universe is provided by fuzzy conditional probability relations. Generalized concepts of rough approximations and rough membership functions are proposed and defined based on coverings of the universe. Such generalization is considered as a kind of fuzzy rough set. A more generalized fuzzy rough set approximation of a given fuzzy set is proposed and discussed as an alternative to provide interval-value fuzzy sets. Their properties are examined.

Measures of uncertainty for a fuzzy probabilistic approximation space

2021 ◽  
pp. 1-24
Author(s):  
Lijun Chen ◽  
Damei Luo ◽  
Pei Wang ◽  
Zhaowen Li ◽  
Ningxin Xie
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Relation ◽  
Point Of View ◽  
Approximation Space ◽  
Fuzzy Approximation ◽  
Entropy Measurement ◽  
Fuzzy Relation Matrix ◽  
Relation Matrix

 An approximation space (A-space) is the base of rough set theory and a fuzzy approximation space (FA-space) can be seen as an A-space under the fuzzy environment. A fuzzy probability approximation space (FPA-space) is obtained by putting probability distribution into an FA-space. In this way, it combines three types of uncertainty (i.e., fuzziness, probability and roughness). This article is devoted to measuring the uncertainty for an FPA-space. A fuzzy relation matrix is first proposed by introducing the probability into a given fuzzy relation matrix, and on this basis, it is expanded to an FA-space. Then, granularity measurement for an FPA-space is investigated. Next, information entropy measurement and rough entropy measurement for an FPA-space are proposed. Moreover, information amount in an FPA-space is considered. Finally, a numerical example is given to verify the feasibility of the proposed measures, and the effectiveness analysis is carried out from the point of view of statistics. Since three types of important theories (i.e., fuzzy set theory, probability theory and rough set theory) are clustered in an FPA-space, the obtained results may be useful for dealing with practice problems with a sort of uncertainty.

scholarly journals On Some Types of Covering-Based ℐ , T -Fuzzy Rough Sets and Their Applications

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Mohammed Atef ◽  
José Carlos R. Alcantud ◽  
Hussain AlSalman ◽  
Abdu Gumaei
Keyword(s):  
Rough Set ◽  
Rough Sets ◽  
Practical Application ◽  
Fuzzy Rough Sets ◽  
Fuzzy Rough Set ◽  
Lower Approximation ◽  
Upper Approximation ◽  
Numerical Example ◽  
Variable Precision

The notions of the fuzzy β -minimal and maximal descriptions were established by Yang et al. (Yang and Hu, 2016 and 2019). Recently, Zhang et al. (Zhang et al. 2019) presented the fuzzy covering via ℐ , T -fuzzy rough set model ( FC ℐ T FRS ), and Jiang et al. (Jiang et al., in 2019) introduced the covering through variable precision ℐ , T -fuzzy rough sets ( CVP ℐ T FRS ). To generalize these models in (Jiang et al., 2019 and Zhang et al. 2019), that is, to improve the lower approximation and reduce the upper approximation, the present paper constructs eight novel models of an FC ℐ T FRS based on fuzzy β -minimal (maximal) descriptions. Characterizations of these models are discussed. Further, eight types of CVP ℐ T FRS are introduced, and we investigate the related properties. Relationships among these models are also proposed. Finally, we illustrate the above study with a numerical example that also describes its practical application.

scholarly journals On rough sets induced by fuzzy relations approach in semigroups

2018 ◽  
Vol 16 (1) ◽  
pp. 1634-1650
Author(s):  
Rukchart Prasertpong ◽  
Manoj Siripitukdet
Keyword(s):  
Prime Ideal ◽  
Rough Set ◽  
Rough Sets ◽  
Sufficient Conditions ◽  
Unit Interval ◽  
Fuzzy Relation ◽  
Prime Ideals ◽  
Fuzzy Relations ◽  
Completely Prime Ideal

AbstractIn this paper, we introduce a rough set in a universal set based on cores of successor classes with respect to level in a closed unit interval under a fuzzy relation, and some interesting properties are investigated. Based on this point, we propose a rough completely prime ideal in a semigroup structure under a compatible preorder fuzzy relation, including the rough semigroup and rough ideal. Then we provide sufficient conditions for them. Finally, the relationships between rough completely prime ideals (rough semigroups and rough ideals) and their homomorphic images are verified.

scholarly journals Some New Covering-Based Multigranulation Fuzzy Rough Sets and Corresponding Application in Multicriteria Decision Making

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Zaibin Chang ◽  
Lingling Mao
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Rough Sets ◽  
Large Scale ◽  
Rough Set Theory ◽  
Multicriteria Decision Making ◽  
Matrix Representations ◽  
Fuzzy Rough Set ◽  
Multicriteria Decision ◽  
Multigranulation Rough Set

Multigranulation rough set theory is an important tool to deal with the problem of multicriteria information system. The notion of fuzzy β -neighborhood has been used to construct some covering-based multigranulation fuzzy rough set (CMFRS) models through multigranulation fuzzy measure. But the β -neighborhood has not been used in these models, which can be seen as the bridge of fuzzy covering-based rough sets and covering-based rough sets. In this paper, the new concept of multigranulation fuzzy neighborhood measure and some types of covering-based multigranulation fuzzy rough set (CMFRS) models based on it are proposed. They can be seen as the further combination of fuzzy sets: covering-based rough sets and multigranulation rough sets. Moreover, they are used to solve the problem of multicriteria decision making. Firstly, the definition of multigranulation fuzzy neighborhood measure is given based on the concept of β -neighborhood. Moreover, four types of CMFRS models are constructed, as well as their characteristics and relationships. Then, novel matrix representations of them are investigated, which can satisfy the need of knowledge discovery from large-scale covering information systems. The matrix representations can be more easily implemented than set representations by computers. Finally, we apply them to manage the problem of multicriteria group decision making (MCGDM) and compare them with other methods.

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