On intuitionistic fuzzy decision‐making using soft likelihood functions

Fei, Liguo; Feng, Yong; Liu, Luning; Mao, Weicheng

doi:10.1002/int.22141

Cited by 37 publications

(17 citation statements)

References 34 publications

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“…Example A fictitious MCDM problem is used from in this example. In a murder case, to find the most likely killer from the six suspects

{A_{1}, …, A_{6}}

( m = 6), five criteria

{C_{1}, …, C_{5}}

are introduced, and the evaluations are expressed by PFNs, which are recorded in Table .…”

Section: Soft Likelihood Function Of Pfs Extended By Owa Operatormentioning

confidence: 99%

“…It has been extended to Dempster‐Shafer evidence theory for combining the basic probability assignments by Jiang and Hu . In our previous research, the concept of soft likelihood for IFSs was proposed based on the ordered weighted averaging (OWA) operators for MCDM. In addition, a novel aggregation method was presented based on OWA operators under interval‐valued fuzzy environments motivated by soft likelihood functions.…”

Section: Introductionmentioning

confidence: 99%

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On Pythagorean fuzzy decision making using soft likelihood functions

2019

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Multicriteria decision making (MCDM) is to select the optimal candidate which has the best quality from a finite set of alternatives with multiple criteria. One important component of MCDM is to express the evaluation information, and the other one is to aggregate the evaluation results associated with different criteria. For the former, Pythagorean fuzzy set (PFS) is employed to represent uncertain information in this paper, and for the latter, the soft likelihood function developed by Yager is used. To address MCDM issues from a new perspective, the likelihood function of PFS is first proposed in this study and, to improve some of its limitations, the ordered weighted averaging (OWA)‐based soft likelihood function is defined, which introduces the attitudinal characteristic to identify decision makers' subjective preferences. In addition, the defined soft likelihood function of PFS is extended by weighted OWA operator considering the importance weight of the argument. Several illustrative cases are provided based on the presented (weighted) OWA‐based soft likelihood functions in Pythagorean fuzzy environment for MCDM problem.

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“…Example A fictitious MCDM problem is used from in this example. In a murder case, to find the most likely killer from the six suspects

{A_{1}, …, A_{6}}

( m = 6), five criteria

{C_{1}, …, C_{5}}

are introduced, and the evaluations are expressed by PFNs, which are recorded in Table .…”

Section: Soft Likelihood Function Of Pfs Extended By Owa Operatormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Pythagorean fuzzy decision making using soft likelihood functions

2019

Self Cite

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“…How to process uncertain and imprecise information is still a problem 2,3 . Many mathematical models and theories have been proposed and utilized to model uncertain information, for instance, the extended soft set, 4,5 intuitionistic fuzzy sets, 6 D‐S evidence theory, 7 complex mass function, 8‐10 z ‐number, 11,12 D ‐number, 13 R ‐sets, 14 and so on. Because of the capability to handle the uncertainty, they have been used in various fields, such as workflow scheduling, 15 risk analysis, 16 complex networks analysis, 17 pattern classification, 18‐21 and multiattribute decision making 22,23 .…”

Section: Introductionmentioning

confidence: 99%

A method for combining conflicting evidences with improved distance function and Tsallis entropy

Xiao

2020

Int J Intell Syst

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For the sake of great ability of handling uncertain information, Dempster‐Shafer evidence theory is extensively used in information fusion. Nevertheless, when there exists highly inconsistent evidences, using classical Dempster's combination rule may lead to counter‐intuitive results. To address this issue, a new conflicting evidences combination method based on distance function and Tsallis entropy is proposed. Numerical examples are used to illustrate the feasibility and efficiency of the proposed method. Further, an fault diagnosis problem is used as an example to show the effectiveness and superiority of the proposed method. The proposed method outperforms other methods that the proposed method recognize the target by the probability 99.49%, which is higher than other methods.

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“…Due to the flexibility of soft‐likelihood functions to process uncertain information, there are many studies on soft‐likelihood functions. Specially, Fei et al 20 proposed a new pythagorean fuzzy decision model based on the soft‐likelihood function, Li et al 21 proposed a new combination rule based on OWA soft‐likelihood function and Dempster‐Shafer theory, and Wang et al 22 based on belied entropy and soft‐Likelihood function studyed the improvement of divergence measure to fuse multisource data and so on.…”

Section: Introductionmentioning

confidence: 99%

A modified soft‐likelihood function based on POWA operator

Tian

Kang

2020

Int J Intell Syst

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Information fusion is an important research direction. In this field, there are plenty of ways to combine evidence. Initially, Yager proposed a soft‐likelihood function based on the ordered weighted average (OWA) operator to effectively fuse compatible probabilistic evidence. Recently, Song et al proposed a new soft‐likelihood function based on the power ordered weighted average (POWA) operator. However, through analysis, we find Song et al's method has the following two shortcomings: (a) The weight of POWA cannot comprehensively reflect the relation between probability and OWA operator. (b) The soft‐likelihood function does not reflect the preferences of decision makers. To overcome the above problem, we propose a modified soft‐likelihood function. The effectiveness of the proposed method is demonstrated from the perspective of theoretical analysis and numerical examples.

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On intuitionistic fuzzy decision‐making using soft likelihood functions

Cited by 37 publications

References 34 publications

On Pythagorean fuzzy decision making using soft likelihood functions

On Pythagorean fuzzy decision making using soft likelihood functions

A method for combining conflicting evidences with improved distance function and Tsallis entropy

A modified soft‐likelihood function based on POWA operator

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