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Aggregation functions based on penalties

Calvo, Tomasa and Beliakov, Gleb 2010, Aggregation functions based on penalties, Fuzzy sets and systems, vol. 161, no. 10, pp. 1420-1436.

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Title Aggregation functions based on penalties
Author(s) Calvo, Tomasa
Beliakov, Gleb
Journal name Fuzzy sets and systems
Volume number 161
Issue number 10
Start page 1420
End page 1436
Publisher Elsevier
Place of publication Amsterdam, Netherlands
Publication date 2010-05
ISSN 0165-0114
1872-6801
Keyword(s) aggregation operators
means
quasi-arithmetic means
median
OWA
penalty function
Summary This article studies a large class of averaging aggregation functions based on minimizing a distance from the vector of inputs, or equivalently, minimizing a penalty imposed for deviations of individual inputs from the aggregated value. We provide a systematization of various types of penalty based aggregation functions, and show how many special cases arise as the result. We show how new aggregation functions can be constructed either analytically or numerically and provide many examples. We establish connection with the maximum likelihood principle, and present tools for averaging experimental noisy data with distinct noise distributions.
Notes Reproduced with the specific permission of the copyright owner.
Language eng
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
HERDC collection year 2010
Copyright notice ©2009, Elsevier B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30028309

Document type: Journal Article
Collections: School of Information Technology
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