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Biased experts and similarity based weights in preferences aggregation

Beliakov, Gleb, James, Simon, Smith, Laura and Wilkin, Tim 2015, Biased experts and similarity based weights in preferences aggregation, in EUSFLAT 2015: Proceedings of the 16th World Congress of the International-Fuzzy-Systems-Association (IFSA) / 9th Conference of the European-Society-for-Fuzzy-Logic-and-Technology, Atlantis Press, Amsterdam, The Netherlands, pp. 363-370, doi: 10.2991/ifsa-eusflat-15.2015.53.

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Title Biased experts and similarity based weights in preferences aggregation
Author(s) Beliakov, GlebORCID iD for Beliakov, Gleb orcid.org/0000-0002-9841-5292
James, SimonORCID iD for James, Simon orcid.org/0000-0003-1150-0628
Smith, Laura
Wilkin, TimORCID iD for Wilkin, Tim orcid.org/0000-0003-4059-1354
Conference name European-Society-for-Fuzzy-Logic-and-Technology World Congress (16th: 2015: Gijon, Spain)
Conference location Gijon, SPAIN
Conference dates 2015/06/30 - 2015/07/03
Title of proceedings EUSFLAT 2015: Proceedings of the 16th World Congress of the International-Fuzzy-Systems-Association (IFSA) / 9th Conference of the European-Society-for-Fuzzy-Logic-and-Technology
Editor(s) Alonso, J. M.
Bustince, H.
Reformat, M.
Publication date 2015
Series Advances in intelligent systems research v.89
Start page 363
End page 370
Total pages 8
Publisher Atlantis Press
Place of publication Amsterdam, The Netherlands
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
aggregation functions
non-monotonic averaging
consensus
pairwise preferences
group decision making
induced OWA
GROUP DECISION-MAKING
CONSENSUS MODEL
OPERATORS
Summary In a group decision making setting, we consider the potential impact an expert can have on the overall ranking by providing a biased assessment of the alternatives that differs substantially from the majority opinion. In the framework of similarity based averaging functions, we show that some alternative approaches to weighting the experts' inputs during the aggregation process can minimize the influence the biased expert is able to exert.
ISBN 978-94-62520-77-6
ISSN 1951-6851
Language eng
DOI 10.2991/ifsa-eusflat-15.2015.53
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, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution non-commercial licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30077923

Document type: Conference Paper
Collections: School of Information Technology
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.