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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 : Proceedings of the 9th Modeling Decisions for Artificial Intelligence International Conference, Springer Berlin Heidelberg, [Girona, Spain], 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 : Girona, Spain)
Conference location Girona, Spain
Conference dates 21-23 Nov. 2012
Title of proceedings MDAI 2012 : Proceedings of the 9th Modeling Decisions for Artificial Intelligence International Conference
Editor(s) Torra, Vicenç
Narukawa, Yasuo
López, Beatriz
Villaret, Mateu
Publication date 2012
Series Lecture notes in computer science v.7647
Conference series Modeling Decisions for Artificial Intelligence International Conference
Start page 35
End page 44
Total pages 10
Publisher Springer Berlin Heidelberg
Place of publication [Girona, Spain]
Keyword(s) Aggregation functions
least absolute deviation (LAD) fitting
weights identification
generalized Bonferroni mean
means
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 9783642346200
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 E2 Full written paper - non-refereed / Abstract reviewed
Copyright notice ©2012, Springer-Verlag Berlin Heidelberg
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049244

Document type: Conference Paper
Collection: School of Information Technology
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