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Usability of Concordance Indices in FAST-GDM Problems

Topics: Applications: Fuzzy Systems in Robotics, Fuzzy Image, Speech and Signal Processing, Vision and Multimedia, Pattern Recognition, Financial and Medical Applications, Fuzzy Information Retrieval and Data Mining, Big Data and Cloud Computing, Industrial and R; Fuzzy Information Processing, Fusion, Text Mining

Authors: Marcelo Loor 1 ; Ana Tapia-Rosero 2 and Guy De Tré 3

Affiliations: 1 Dept. of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, B-9000, Ghent, Belgium, Dept. of Electrical and Computer Engineering, ESPOL Polytechnic University, Campus Gustavo Galindo V., Km. 30.5 Via Perimetral, Guayaquil and Ecuador ; 2 Dept. of Electrical and Computer Engineering, ESPOL Polytechnic University, Campus Gustavo Galindo V., Km. 30.5 Via Perimetral, Guayaquil and Ecuador ; 3 Dept. of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstraat 41, B-9000, Ghent and Belgium

ISBN: 978-989-758-327-8

Keyword(s): Flexible Consensus Reaching, Group Decision-Making, Intuitionistic Fuzzy Sets, IFS Contrasting Charts.

Abstract: A flexible attribute-set group decision-making (FAST-GDM) problem boils down to finding the most suitable option(s) with a general agreement among the participants in a decision-making process in which each option can be described by a flexible collection of attributes. The solution to such a problem can involve a consensus reaching process (CRP) in which the participants iteratively try to reach a general agreement on the best option(s) based on the attributes that are relevant for each participant. A challenging task in a CRP is the selection of an adequate method to determine the level of concordance between the evaluations given by each participant and the collective evaluations computed for the group. To gain insights in this regard, we performed a pilot test in which a group of persons were asked to estimate the level of concordance between individual and collective evaluations obtained while other participants tried to solve a FAST-GDM problem. The perceived concordance levels were compared with several theoretical concordance indices based on similarity measures designed to compare intuitionistic fuzzy sets. This paper presents our findings on how each of the chosen theoretical concordance indices reflected the perceived concordance levels. (More)

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Paper citation in several formats:
Loor M., Tapia-Rosero A. and De Tré G. (2018). Usability of Concordance Indices in FAST-GDM Problems.In Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI, ISBN 978-989-758-327-8, pages 67-78. DOI: 10.5220/0006956500670078

@conference{ijcci18,
author={Marcelo Loor and Ana Tapia-Rosero and Guy De Tré},
title={Usability of Concordance Indices in FAST-GDM Problems},
booktitle={Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,},
year={2018},
pages={67-78},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006956500670078},
isbn={978-989-758-327-8},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Computational Intelligence - Volume 1: IJCCI,
TI - Usability of Concordance Indices in FAST-GDM Problems
SN - 978-989-758-327-8
AU - Loor M.
AU - Tapia-Rosero A.
AU - De Tré G.
PY - 2018
SP - 67
EP - 78
DO - 10.5220/0006956500670078

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