I'm doing classification with two classes. Before I apply a classifier, I'm doing some preprocessing steps like removing near-zero variance features or highly correlated features (for those classifiers which are sensitive to it).
Now I will also add a feature selection step (ReliefF and genetic algorithms). Do I still have to do the above preprocessing steps before or after the feature selection or is this already incorporated in the feature selection? I think the feature selection process should already eliminate correlated and near-zero variance features but I'm not completely sure. Of course standardization and missing value imputation I have to do before the feature selection.