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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 4/2021

30-09-2020 | Original Article

A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets

Authors: Peng Li, Nannan Wang, Cuiping Wei, Na Zhang

Published in: International Journal of Machine Learning and Cybernetics | Issue 4/2021

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Abstract

In order to solve matching problems for probabilistic linguistic information, a novel two-sided matching decision method for the probabilistic linguistic term sets (PLTSs) based on the regret theory considering the lowest value of acceptability is proposed. First, we propose a new utility function to transform the PLTSs to utility values, which can be conveniently applied to two-sided matching models. Then, to reflect the bounded rationality of expert and make the decision result close to real decision process, we put forward a novel regret-based model to obtain regret-rejoice by setting the lowest value of acceptability based on the utility function. Furthermore, we presented a new type of two-sided matching method considering constraint condition based on the lowest value of acceptability. Finally, we apply our method to a real case and make comparisons with two traditional two-sided methods.
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Metadata
Title
A two-sided matching method considering the lowest value of acceptability with regret theory for probabilistic linguistic term sets
Authors
Peng Li
Nannan Wang
Cuiping Wei
Na Zhang
Publication date
30-09-2020
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 4/2021
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-020-01211-6

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