I would like to perform a combination of oversampling and undersampling in order to balance my dataset with roughly 4000 customers divided into two groups, where one of the groups have a proportion of roughly 15%.
I've looked into SMOTE (http://www.inside-r.org/packages/cran/DMwR/docs/SMOTE) and ROSE (http://cran.r-project.org/web/packages/ROSE/ROSE.pdf), but both of these create new synthetic samples using existing observations and e.g. kNN.
However, as many of the attributes associated with the customers are categorical I don't think this is the right way to go. For instance, a lot of my variables such as Region_A and Region_B are mutually exclusive, but using kNN the new observations may be placed in both Region_A and Region_B. Do you agree that this is an issue?
In that case - how do one perform oversampling in R by simply duplicating existing observations? Or is this the wrong way to do it?