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Using linear programming for weights identification of generalized bonferroni means in R

Beliakov, Gleb and James, Simon 2012, Using linear programming for weights identification of generalized bonferroni means in R, in MDAI 2012 : Modeling decisions for artificial intelligence : 9th International Conference, Girona, Catalonia, Spain, November 2012 : proceedings, Springer, Berlin, Germany, pp. 35-44.

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Title Using linear programming for weights identification of generalized bonferroni means in R
Author(s) Beliakov, Gleb
James, Simon
Conference name Modeling Decisions for Artificial Intelligence. Conference (9th : 2012 : Catalonia, Spain)
Conference location Catalonia, Spain
Conference dates 21-23 Nov. 2012
Title of proceedings MDAI 2012 : Modeling decisions for artificial intelligence : 9th International Conference, Girona, Catalonia, Spain, November 2012 : proceedings
Editor(s) Torra, Vicenc
Narukawa, Yasuo
Lopez, Beatriz
Villaret, Mateu
Publication date 2012
Series Lecture Notes in Artificial Intelligence ; v.7647
Conference series Modeling Decisions for Artificial Intelligence Conference
Start page 35
End page 44
Total pages 10
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) aggregation functions
generalized Bonferroni mean
least absolute deviation (LAD) fitting
means
weights identification
Summary The generalized Bonferroni mean is able to capture some interaction effects between variables and model mandatory requirements. We present a number of weights identification algorithms we have developed in the R programming language in order to model data using the generalized Bonferroni mean subject to various preferences. We then compare its accuracy when fitting to the journal ranks dataset.
ISBN 9783642346194
ISSN 0302-9743
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 E1 Full written paper - refereed
Copyright notice ©2012, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051350

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.