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Learning aggregation weights from 3-tuple comparison sets
conference contribution
posted on 2013-01-01, 00:00 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James, Dale NimmoAn 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.
History
Event
Fuzzy Systems and NAFIPS. Joint World Congress and Annual Meeting (9th : 2013 : Edmonton, Alberta)Pagination
1388 - 1393Publisher
IEEELocation
Edmonton, AlbertaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2013-06-24End date
2013-06-28ISBN-13
9781479903474Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2013, IEEETitle of proceedings
IFSA/NAFIPS 2013 : Proceedings of the 9th Joint IFSA World Congress and NAFIPS Annual MeetingUsage metrics
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