I'm training a (regression) learner on a $p \gg n$ problem, including bagging and filter feature selection (information gain).
I'm in doubt though regarding the order of the procedures:
Apply the filter first, then bagging randomly select features from these only.
Apply the filter after randomly selecting features for bagging.
I can see the filter reduces the randomization of bagging if applied first. Still, I'm more inclined towards the first approach. Is there any argument against/for either?
Edit: I can see the second option should take into consideration computational resource constraints as well, but let's focus on the statistical implications of such choice.