Intuitionistic Fuzzy Power Aggregation Operator Based on Entropy and Its Application in Decision Making

Jiang, Wen; Wei, Boya; Xiang, Liang; Li, Xiaoyang; Zheng, Hanqing

doi:10.1002/int.21939

Cited by 111 publications

(69 citation statements)

References 60 publications

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“…Fuzzy logic was developed by Zadeh and Mamdani and Assilian, based on which the concept of approximate reasoning was introduced and showed that vague logical statements enable the formation of algorithms that can use vague data to derive vague inferences. Many theories have been promoted by this method, such as Dempster‐Shafer theory, Z‐numbers, and fuzzy reasoning . The definition of fuzzy sets is given as follows:…”

Section: Fuzzy Sets and Ivfssmentioning

confidence: 99%

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

Fei

2019

Int J Intell Syst

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Multicriteria decision‐making approaches have been studied very widely in recent years and are frequently used in many real‐life applications. To select the optimal alternative that satisfies the multicriteria, effective aggregation methods are crucial in the decision‐making process. The soft likelihood functions (SLF) developed by Yager is introduced as preliminaries of our research, which is a flexible aggregation approach of probabilistic evidence in the context of forensic crime investigations. Motivated by SLF, in this study, a novel aggregation method is proposed based on ordered weighted averaging operator under interval‐valued fuzzy environments. To improve the performance of the new aggregation method, the reliability is taken into account in the decision‐making process from two perspectives. The first is the reliability of assessment value, including certainty and compatibility, for discounting assessment information; the second is human reliability for redefining SLF. Some numerical examples are given to demonstrate the proposed decision approach. And the reliability‐based aggregation method is illustrated more reasonable than the one without considering reliability.

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Section: Fuzzy Sets and Ivfssmentioning

confidence: 99%

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

Fei

2019

Int J Intell Syst

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“… Alternative decision entropyEntropy is a measure of uncertainty. It uses the characteristics of information entropy to measure consistency among decision makers, such as weight adjustment algorithm based on entropy theory (Xia & Xu, ) and aggregator optimization (Jiang, Wei, Liu, Li, & Zheng, ). We propose a kind of alternative decision entropy ( E i ) index for individual alternative evaluation value to measure the consistency of group decision‐making alternatives, which is shown as follows.…”

Section: Decision‐making Processmentioning

confidence: 99%

A multi‐agent affective interactive MAGDM approach and its applications

Peng

Sangwoo

2019

Expert Systems

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Traditional multi‐attribute group decision‐making (MAGDM) methods focus on weights calculation of sub‐attributes and experts' preferences, but lack the discussion on the decision‐makers' affective interaction, and its influence on the decision preference and group consistency. To address this problem, the present study proposed a new multilayer affective computing model based on “personality–mood–emotion” pattern, under the multi‐agent decision system framework. In addition, we introduced the group trending index and affection‐preference incentive mechanism, which can help simulate MAGDM process and learn group experts' decision preferences. Further, we proposed a new multi‐agent affective interactive MAGDM (MAAI‐MAGDM) method, where we defined a novel group convergence index and an alternative decision entropy to explain the convergence process of decision and group consistency. Compared to the traditional MAGDM approaches, the proposed MAAI‐MAGDM method fully considered the affective features of each expert, reduced the dependence on aggregation operators and weight analysis, alleviated the workload of group experts, and effectively reduced the complexity of decision‐making calculation process. Finally, we verified that the proposed method can effectively assist the decision‐making processes by employing two numerical cases.

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“…Information fusion is to detect, combine, correlate, estimate, and combine multisource data to achieve accurate state estimation and identity estimation. Information fusion technology is widely used in reliability analysis, image processing, intelligent transportation systems, and so on. The advantages of information fusion can be summarized as follows: The information fusion system fuses the information from different sources and overcomes the one‐sidedness of the single information source.…”

Section: Introductionmentioning

confidence: 99%

An improved soft likelihood function for Dempster-Shafer belief structures

Jiang

2018

Int. J. Intell. Syst.

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Information fusion is an important research direction. In the field of information fusion, there are many methods for evidence combination. Recently, Yager proposed a method of soft likelihood function to combine probabilistic evidence effectively. Considering that basic probability assignment (BPA) can deal with uncertainty information more effectively, in this paper, we extend Yager's soft likelihood function to combine BPA. First, according to the BPA evaluations of evidence sources, belief function and plausibility function on each alternative are calculated. Then, interval numbers are constructed by the obtained belief function and plausibility function to indicate the belief interval on each alternative. Next, the descending sorting of interval numbers is aggregated by the ordered weighted averaging operator. Finally, by sorting the result of the aggregation, the ordering of alternatives is obtained. A numerical example and an example of application in Iris data set classification illustrate the effectiveness of the improved method.

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Intuitionistic Fuzzy Power Aggregation Operator Based on Entropy and Its Application in Decision Making

Cited by 111 publications

References 60 publications

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

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

A multi‐agent affective interactive MAGDM approach and its applications

An improved soft likelihood function for Dempster-Shafer belief structures

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