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Construction and aggregation of preference relations based on fuzzy partial orders

Beliakov, Gleb, James, Simon and Wilkin, Tim 2015, Construction and aggregation of preference relations based on fuzzy partial orders, in FUZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems, IEEE, Piscataway, N.J., pp. 1-8, doi: 10.1109/FUZZ-IEEE.2015.7338066.

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Title Construction and aggregation of preference relations based on fuzzy partial orders
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
James, Simon
Wilkin, TimORCID iD for Wilkin, Tim orcid.org/0000-0003-4059-1354
Conference name IEEE International Conference on Fuzzy Systems (2015 : Istanbul, Turkey)
Conference location Istanbul, TURKEY
Conference dates 2-5 Aug. 2015
Title of proceedings FUZZ-IEEE 2015: Proceedings of the IEEE International Conference on Fuzzy Systems
Editor(s) Yazici, A.
Pal, N. R.
Kaymak, U.
Martin, T.
Ishibuchi, H.
Lin, C. T.
Sousa, J. M. C.
Tutmez, B.
Publication date 2015
Series IEEE International Fuzzy Systems Conference Proceedings
Start page 1
End page 8
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Engineering, Electrical & Electronic
Engineering
GROUP DECISION-MAKING
CONSENSUS MODEL
CONSISTENCY
OPERATORS
Summary In group decision-making problems it is common to elicit preferences from human experts in the form of pairwise preference relations. When this is extended to a fuzzy setting, entries in the pairwise preference matrix are interpreted to denote strength of preference, however once logical properties such as consistency and transitivity are enforced, the resulting preference relation requires almost as much information as providing raw scores or a complete order over the alternatives. Here we instead interpret fuzzy degrees of preference to only apply where the preference over two alternatives is genuinely fuzzy and then suggest an aggregation procedure that minimizes a generalized Kemeny distance to the nearest complete or partial order. By focusing on the fuzzy partial order, the method is less affected by differences in the natural scale over which an expert expresses their preference, and can also limit the influence of extreme scores.
ISBN 9781467374286
ISSN 1544-5615
Language eng
DOI 10.1109/FUZZ-IEEE.2015.7338066
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082924

Document type: Conference Paper
Collection: School of Information Technology
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