On interval‐valued fuzzy decision‐making using soft likelihood functions

Fei, Liguo

doi:10.1002/int.22110

Cited by 36 publications

(16 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The OWA operator was first proposed by Yager. 5 Due to its flexibility in handling uncertain information, it has been used in a wide range of applications, such as data mining, 24,25 decision making, 26,27 and information fusion, 19,28,29 and so on. The definition of the OWA operator is as follows.…”

Section: Owa Operatormentioning

confidence: 99%

A modified soft‐likelihood function based on POWA operator

Tian

Kang

2020

Int J Intell Syst

View full text Add to dashboard Cite

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.

show abstract

Section: Owa Operatormentioning

confidence: 99%

A modified soft‐likelihood function based on POWA operator

Tian

Kang

2020

Int J Intell Syst

View full text Add to dashboard Cite

show abstract

“…In DST, the distance measure is an important concept, which denotes the degree of dissimilarity between sources of evidence. A variety of distance measures of evidence have been developed and widely applied, such as the Jousselme evidence distance, the belief interval‐based distance measure, and other measures . Also, an appropriate dissimilarity measure could be selected according to the actual situation.…”

Section: Considering Reliability In the Novel Combination Rulementioning

confidence: 99%

Evidence combination using OWA‐based soft likelihood functions

2019

Self Cite

View full text Add to dashboard Cite

Dempster's combination rule has been widely regarded and applied since it is an effective and rigorous method of synthesizing multisource information with its special information representation (ie, mass function or basic probability assignment). However, it has also been criticized and debated upon regarding some of its unreasonable behaviors and restrictive requirements, such as the counterintuitive results in some cases. To address these issues from different perspectives, in this study, an alternative fusion rule is developed under the framework of Dempster‐Shafer evidence theory. A novel evidence combination rule called CR‐SLF is proposed based on soft likelihood functions (SLF) considering the ordered weighted average aggregation operator. Some illustrative examples are shown, and the corresponding analyses demonstrate the good performance of CR‐SLF to fuse multisource evidence. To extend CR‐SLF further, the reliability of multisource evidence is considered from two aspects, subsequently two reliability‐based combination rules are presented, including the discount‐based rule and the SLF improvement‐based rule. The simulation results show that the reliability‐based CR‐SLF has a better fusion effect than the rule without considering the reliability.

show abstract

“…22 In our previous research, [23][24][25] 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 26 was presented based on OWA operators under interval-valued fuzzy environments motivated by soft likelihood functions.…”

Section: Introductionmentioning

confidence: 99%

On Pythagorean fuzzy decision making using soft likelihood functions

2019

Self Cite

View full text Add to dashboard Cite

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.

show abstract

On interval‐valued fuzzy decision‐making using soft likelihood functions

Cited by 36 publications

References 55 publications

A modified soft‐likelihood function based on POWA operator

A modified soft‐likelihood function based on POWA operator

Evidence combination using OWA‐based soft likelihood functions

On Pythagorean fuzzy decision making using soft likelihood functions

Contact Info

Product

Resources

About