A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method

Jin, Yun; Wu, Huai‐Ning; Sun, Dechao; Zeng, Shouzhen; Luo, Dandan; Peng, Bo

doi:10.3390/math7100999

Cited by 15 publications

(13 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next in Examples 6 and 7, we show that the PF correlation coefficients C GSB2 (Ganie et al 2020) and C JWSZLP (Jin et al 2019) fail to compute the correlation degree of those PFSs in which either of the membership grades is zero.…”

Section: Computation Of Correlation Between Different Pfssmentioning

confidence: 83%

“…In the PF environment, Singh ( 2015 ) introduced some correlation coefficients and demonstrated their effectiveness in bidirectional approximate reasoning besides presenting the PF clustering algorithm. Jin et al ( 2019 ) introduced Pearson’s picture fuzzy correlation coefficient with its application to decision-making. Recently, Ganie et al ( 2020 ) introduced some new correlation coefficients in the PF environment and demonstrated their application in pattern recognition, medical diagnosis, and clustering.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making

Singh

Ganie

2021

Granul. Comput.

View full text Add to dashboard Cite

Picture fuzzy set is an efficient tool for dealing with uncertainty and vagueness, particularly in situations that require assimilation of more dimensions of linguistic assessment such as human voting, feature selection, etc. The correlation coefficient of picture fuzzy sets is a tool to determine the association of two picture fuzzy sets. It has several applications in various disciplines like science, engineering, and management. The prominent applications include decision-making, pattern recognition, clustering analysis, medical diagnosis, etc. In this paper, we introduce a new correlation coefficient for picture fuzzy sets with the justification of its advantages. This correlation coefficient is better than the existing correlation coefficients and other such measures in the picture fuzzy theory because it considers the picture fuzzy set as a whole and also expresses the nature (positive or negative) as well as the extent of association between two PFSs. By performing some comparative analysis based on the computation of correlation degree and linguistic hedges, we establish the effectiveness of the suggested correlation measure over some available correlation measures in a picture fuzzy environment. Further, in the context of pattern recognition, we examine the performance of the proposed correlation measure over some existing picture fuzzy correlation measures. Finally, we apply the suggested picture fuzzy correlation coefficient to a decisionmaking problem involving the selection of an appropriate COVID-19 mask.

show abstract

Section: Computation Of Correlation Between Different Pfssmentioning

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making

Singh

Ganie

2021

Granul. Comput.

View full text Add to dashboard Cite

show abstract

“…Wang et al (2020b) presented a bounded rationality behavioural model with PFNs to address MCDM issues. Jin et al (2019) developed a model which was related to Pearson's picture fuzzy correlation for MCDM analysis. Son (2017) extended the fundamental distance measure in the context of PFSs.…”

Section: Review About Picture Fuzzy Setmentioning

confidence: 99%

Copras Method for Multiple Attribute Group Decision Making Under Picture Fuzzy Environment and Their Application to Green Supplier Selection

Zhang

et al. 2021

Technological and Economic Development of Economy

105

View full text Add to dashboard Cite

The green supplier selection (GSS) is a significant part in green supply chain management (GSCM). Choosing optimal green supplier can not only realize the sustainable development of enterprises, but also maximize the utilization rate of resources and diminish the negative effect of environmental issues, which conforms to the theme of green development. As a multiple attribute group decision-making (MAGDM) issue, selecting optimal green supplier is of vital important to enterprises. However, how to select the optimal supplier for enterprises is a great challenge. To handle this issue, a novel picture fuzzy COPRAS (COmplex PRoportional Assessment) method is devised. First, some necessary theories related to picture fuzzy sets (PFSs) are briefly reviewed. In addition, a method called CRITIC (Criteria Importance Though Intercrieria Correlation) is utilized to calculate criteria’s weights. Afterwards, the conventional COPRAS method is extended to the PFSs to calculate each alternative’s utility degree. At last, the designed method is exacted to an application which is related to GSS and there also conduct some comparative analysis to demonstrate the designed method’s superiority. The final results show that the proposed model can be utilized to decide the optimum green supplier.

show abstract

“…To handle emergency decision-making (EDM) problems, a new EDM approach with the help of PFSs was proposed by Ding et al [63]. Jin et al [64] introduced Pearson's correlation-based MADM method in PF environment. Zeng et al [65] introduced an exponential Jensen picture fuzzy divergence measure with its application in MCDM.…”

Section: Introductionmentioning

confidence: 99%

An innovative picture fuzzy distance measure and novel multi-attribute decision-making method

Ganie

Singh

2021

Complex Intell. Syst.

View full text Add to dashboard Cite

Picture fuzzy set (PFS) is a direct generalization of the fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). The concept of PFS is suitable to model the situations that involve more answers of the type yes, no, abstain, and refuse. In this study, we introduce a novel picture fuzzy (PF) distance measure on the basis of direct operation on the functions of membership, non-membership, neutrality, refusal, and the upper bound of the function of membership of two PFSs. We contrast the proposed PF distance measure with the existing PF distance measures and discuss the advantages in the pattern classification problems. The application of fuzzy and non-standard fuzzy models in the real data is very challenging as real data is always found in crisp form. Here, we also derive some conversion formulae to apply proposed method in the real data set. Moreover, we introduce a new multi-attribute decision-making (MADM) method using the proposed PF distance measure. In addition, we justify necessity of the newly proposed MADM method using appropriate counterintuitive examples. Finally, we contrast the performance of the proposed MADM method with the classical MADM methods in the PF environment.

show abstract

A Multi-Attribute Pearson’s Picture Fuzzy Correlation-Based Decision-Making Method

Cited by 15 publications

References 27 publications

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making

Applications of a picture fuzzy correlation coefficient in pattern analysis and decision-making

Copras Method for Multiple Attribute Group Decision Making Under Picture Fuzzy Environment and Their Application to Green Supplier Selection

An innovative picture fuzzy distance measure and novel multi-attribute decision-making method

Contact Info

Product

Resources

About