New Similarity Measures between Vague Sets and Performance Analysis

Wang, Hong Xu; Wang, Hai Feng; Zhang, Kun; Wang, Hui

doi:10.4028/www.scientific.net/amr.811.547

“…At present, there are many methods to solve the similarity of Vague values.This paper chooses the method given in paper (Wang 2013) to solve the similarity of Vague values. There are…”

Section: Evidence Information Aggregation Based On Evidence Theorymentioning

confidence: 99%

Research on Vague multi-attribute group decision-making method based on evidence theory

Cui

¹

,

Cao

²

2022

Preprint

1

0

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In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.

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Similarity measure for multi-granularity rough approximations of vague sets

Yang

¹

,

Zhou

²

,

Li

³

2021

IFS

5

0

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Vague sets are a further extension of fuzzy sets. In rough set theory, target concept can be characterized by different rough approximation spaces when it is a vague concept. The uncertainty measure of vague sets in rough approximation spaces is an important issue. If the uncertainty measure is not accurate enough, different rough approximation spaces of a vague concept may possess the same result, which makes it impossible to distinguish these approximation spaces for charactering a vague concept strictly. In this paper, this problem will be solved from the perspective of similarity. Firstly, based on the similarity between vague information granules(VIGs), we proposed an uncertainty measure with strong distinguishing ability called rough vague similarity (RVS). Furthermore, by studying the multi-granularity rough approximations of a vague concept, we reveal the change rules of RVS with the changing granularities and conclude that the RVS between any two rough approximation spaces can degenerate to granularity measure and information measure. Finally, a case study and related experiments are listed to verify that RVS possesses a better performance for reflecting differences among rough approximation spaces for describing a vague concept.

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Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

Cui

¹

,

Cao

²

2022

IFS

0

View full text Add to dashboard Cite

In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.

show abstract

New Similarity Measures between Vague Sets and Performance Analysis

Cited by 3 publications

References 4 publications

Research on Vague multi-attribute group decision-making method based on evidence theory

Research on Vague multi-attribute group decision-making method based on evidence theory

Similarity measure for multi-granularity rough approximations of vague sets

Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

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