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Fitting generated aggregation operators to empirical data

Beliakov, Gleb, Mesiar, Radko and Valaskova, Lubka 2004, Fitting generated aggregation operators to empirical data, International journal of uncertainty, fuzziness, and knowledge-based systems, vol. 12, no. 2, pp. 219-236.

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Title Fitting generated aggregation operators to empirical data
Author(s) Beliakov, Gleb
Mesiar, Radko
Valaskova, Lubka
Journal name International journal of uncertainty, fuzziness, and knowledge-based systems
Volume number 12
Issue number 2
Start page 219
End page 236
Publisher World Scientific
Place of publication Singapore
Publication date 2004-04
ISSN 0218-4885
1793-6411
Keyword(s) aggregation operators
choquet integral
generated operators
triangular norms
uninorms
Summary This paper treats the problem of fitting general aggregation operators with unfixed number of arguments to empirical data. We discuss methods applicable to associative operators (t-norms, t-conorms, uninorms and nullnorms), means and Choquet integral based operators with respect to a universal fuzzy measure. Special attention is paid to k-order additive symmetric fuzzy measures.
Notes Electronic version of an article published as International Journal of Uncertainty, Fuzziness, and Knowledge-Based Systems, 12(2), 2004, pp.219-236 DOI:10.1142/S0218488504002783 © 2004, World Scientific Publishing Company http://www.worldscinet.com/ijufks/ijufks.shtml
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
Copyright notice ©2004, World Scientific Publishing Company
Persistent URL http://hdl.handle.net/10536/DRO/DU:30002356

Document type: Journal Article
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.