Learning aggregation weights from 3-tuple comparison sets

Beliakov, Gleb, James, Simon and Nimmo, Dale 2013, Learning aggregation weights from 3-tuple comparison sets, in IFSA/NAFIPS 2013 : Proceedings of the 9th Joint IFSA World Congress and NAFIPS Annual Meeting, IEEE, Piscataway, N.J., pp. 1388-1393.

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Title Learning aggregation weights from 3-tuple comparison sets
Author(s) Beliakov, Gleb
James, Simon
Nimmo, Dale
Conference name Fuzzy Systems and NAFIPS. Joint World Congress and Annual Meeting (9th : 2013 : Edmonton, Alberta)
Conference location Edmonton, Alberta
Conference dates 24-28 Jun. 2013
Title of proceedings IFSA/NAFIPS 2013 : Proceedings of the 9th Joint IFSA World Congress and NAFIPS Annual Meeting
Editor(s) [Unknown]
Publication date 2013
Conference series Joint IFSA World Congress and NAFIPS Annual Meeting
Start page 1388
End page 1393
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary An important task in multiple-criteria decision making is how to learn the weights and parameters of an aggregation function from empirical data. We consider this in the context of quantifying ecological diversity, where such data is to be obtained as a set of pairwise comparisons specifying that one community should be considered more diverse than another. A problem that arises is how to collect a sufficient amount of data for reliable model determination without overloading individuals with the number of comparisons they need to make. After providing an algorithm for determining criteria weights and an overall ranking from such information, we then investigate the improvement in accuracy if ranked 3-tuples are supplied instead of pairs. We found that aggregation models could be determined accurately from significantly fewer 3-tuple comparisons than pairs. © 2013 IEEE.
ISBN 9781479903474
Language eng
Field of Research 080199 Artificial Intelligence and Image Processing not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
HERDC collection year 2013
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058800

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