I have an imbalanced (two-class) classification dataset, based on which I am trying to train and cross-validate a classifier.
During the process of the k-fold cross-validation, I set aside the test subsets before I oversample the remaining (training) subsets. I also standardize (zscore) the training subsets, and I store the Mu and Sigma to be later used in standardizing the test subsets.
The problem is that I do not know whether I should do the oversampling first or the standardization first! Any recommendations/suggestions would be appreciated.