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, doi: 10.1007/978-3-642-34620-0_5.
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.
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.
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.
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.