Multi-attribute group decision making using combined ranking value under interval type-2 fuzzy environment

Qin, Jindong; Liu, Xinwang

doi:10.1016/j.ins.2014.11.022

Cited by 124 publications

(39 citation statements)

References 91 publications

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“…Figure 7 shows the 6-support consensus scores with the weighting parameters on the deviations of positions and position gaps being γ = 0.9, λ = 0.9. It can be noticed that queries 58, 25, 24, 55 have higher consensus scoreκ 2 (6), which indicates that the rankings of these queries share more weighted pairwise q-support patterns. Moreover, the consensus scoreκ 1 (6) for these queries are also high.…”

Section: B Evaluation Of the Information Retrieval Results Of The 20mentioning

confidence: 98%

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Quantifying consensus of rankings based on q-support patterns

Zhi

Lin

Wang

et al. 2020

Information Sciences

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Rankings, representing preferences over a set of candidates, are widely used in many information systems, e.g., group decision making and information retrieval. It is of great importance to evaluate the consensus of the obtained rankings from multiple agents. An overall measure of the consensus degree provides an insight into the ranking data. Moreover, it could provide a quantitative indicator for consensus comparison between groups and further improvement of a ranking system. Existing studies are insufficient in assessing the overall consensus of a ranking set. They did not provide an evaluation of the consensus degree of preference patterns in most rankings. In this paper, a novel consensus quantifying approach, without the need for any correlation or distance functions as in existing studies of consensus, is proposed based on a concept of q-support patterns of rankings. The q-support patterns represent the commonality embedded in a set of rankings. A method for detecting outliers in a set of rankings is naturally derived from the proposed consensus quantifying approach. Experimental studies are conducted to demonstrate the effectiveness of the proposed approach.

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Section: B Evaluation Of the Information Retrieval Results Of The 20mentioning

confidence: 98%

“…Average Kendalls τ for the ranking sets of the 66 queries obtained by the 12 teams Average Spearmans ρ for the ranking sets of the 66 queries obtained by the 12 teams (a) Consensus scoreκ 1(6) (b) Consensus scoreκ 2 (Consensus scores without weighting for the ranking sets of the 66 queries obtained by the 12 teams…”

mentioning

confidence: 99%

Quantifying consensus of rankings based on q-support patterns

Zhi

Lin

Wang

et al. 2020

Information Sciences

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“…Even though, HFLTSs and EHFLTSs are effective and useful tools for computing with words because: (1) they extend the traditional linguistic representational models; (2) their semantics are clear. The total orders of type-2 fuzzy sets can be found in Qin and Liu [43].…”

Section: Remarkmentioning

confidence: 99%

Total orders of extended hesitant fuzzy linguistic term sets: Definitions, generations and applications

Wang

Zhang

2016

Knowledge-Based Systems

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“…Therefore, in order to better understand the vagueness and uncertainty of the real world and being able to explain it, Zadeh 1 proposed fuzzy set theory in 1960s. Since its inception, the fuzzy set theory has shown convenience as a powerful tool for modeling vagueness and uncertainty in a variety domains, such as economics 2,3,4 , management 5,6,7,8 , artificial intelligence 16 , processing control 10,11 , pattern recognition 12 , decision making 13,14,15,16 etc. In 1980s, Atanassov 17,18 generalized fuzzy set theory by bringing an idea of intuitionistic fuzzy set (IFS).…”

Section: Introductionmentioning

confidence: 99%

Multi-attribute group decision making based on Choquet integral under interval-valued intuitionistic fuzzy environment

Qin¹,

Liu²,

Pedrycz³

2016

IJCIS

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In this paper, we propose new methods to represent interdependence among alternative attributes and experts' opinions by constructing Choquet integral using interval-valued intuitionistic fuzzy numbers. In the sequel, we apply these methods to solve the multiple attribute group decision-making (MAGDM) problems under interval-valued intuitionistic fuzzy environment. First, the concept of interval-valued intuitionistic fuzzy Choquet integral is defined, and some elementary properties are studied in detail. Next, an axiomatic system of interval-valued intuitionistic fuzzy measure is established by delivering a series of mathematical proofs. Then, with fuzzy entropy and Shapely-values in game theory, we propose the interval-valued intuitionistic fuzzy measure development methods in order to form the importance measure of attributes and correlation measure of the experts, respectively. Based on the results of theoretical analysis, a new method is proposed to handle the interval-valued intuitionistic fuzzy group decision making problems. A numerical example illustrates the procedure of the proposed methods and verifies the validity and effectiveness of our new proposed methods.Keywords: Interval-valued intuitionistic fuzzy Choquet integral, interval-valued intuitionistic fuzzy sets, interval-valued intuitionistic fuzzy measure , multiple attribute group decision making.

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Multi-attribute group decision making using combined ranking value under interval type-2 fuzzy environment

Cited by 124 publications

References 91 publications

Quantifying consensus of rankings based on q-support patterns

Quantifying consensus of rankings based on q-support patterns

Total orders of extended hesitant fuzzy linguistic term sets: Definitions, generations and applications

Multi-attribute group decision making based on Choquet integral under interval-valued intuitionistic fuzzy environment

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