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

Authors:
Gleb Beliakov;Simon James
Affiliations:
School of Information Technology, Deakin University, Burwood, Australia;School of Information Technology, Deakin University, Burwood, Australia
Venue:
MDAI'12 Proceedings of the 9th international conference on Modeling Decisions for Artificial Intelligence
Year:
2012

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Abstract

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.