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Article

Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems

by 1,2,*,†, 2,*,† and 1,†
1
Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, B-9000 Ghent, Belgium
2
Department of Electrical and Computer Engineering, ESPOL Polytechnic University, Campus Gustavo Galindo V. Km. 30.5 Vía Perimetral, P.O. Box 09-01-5863, Guayaquil, Ecuador
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Mathematics 2021, 9(1), 93; https://doi.org/10.3390/math9010093
Received: 1 December 2020 / Revised: 18 December 2020 / Accepted: 28 December 2020 / Published: 4 January 2021
(This article belongs to the Special Issue Intuitionistic Fuzzy Sets and Applications)
A flexible attribute-set group decision-making (FAST-GDM) problem consists in finding the most suitable option(s) out of the options under consideration, with a general agreement among a heterogeneous group of experts who can focus on different attributes to evaluate those options. An open challenge in FAST-GDM problems is to design consensus reaching processes (CRPs) by which the participants can perform evaluations with a high level of consensus. To address this challenge, a novel algorithm for reaching consensus is proposed in this paper. By means of the algorithm, called FAST-CR-XMIS, a participant can reconsider his/her evaluations after studying the most influential samples that have been shared by others through contextualized evaluations. Since exchanging those samples may make participants’ understandings more like each other, an increase of the level of consensus is expected. A simulation of a CRP where contextualized evaluations of newswire stories are characterized as augmented intuitionistic fuzzy sets (AIFS) shows how FAST-CR-XMIS can increase the level of consensus among the participants during the CRP. View Full-Text
Keywords: augmented intuitionistic fuzzy sets; contextualized evaluations; group decision-making; recurrent evaluations; consensus reaching process; computational intelligence; explainable artificial intelligence; explainable support vector machine classification augmented intuitionistic fuzzy sets; contextualized evaluations; group decision-making; recurrent evaluations; consensus reaching process; computational intelligence; explainable artificial intelligence; explainable support vector machine classification
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MDPI and ACS Style

Loor, M.; Tapia-Rosero, A.; De Tré, G. Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems. Mathematics 2021, 9, 93. https://doi.org/10.3390/math9010093

AMA Style

Loor M, Tapia-Rosero A, De Tré G. Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems. Mathematics. 2021; 9(1):93. https://doi.org/10.3390/math9010093

Chicago/Turabian Style

Loor, Marcelo, Ana Tapia-Rosero, and Guy De Tré. 2021. "Towards Better Concordance among Contextualized Evaluations in FAST-GDM Problems" Mathematics 9, no. 1: 93. https://doi.org/10.3390/math9010093

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