I want to use feature selection and observation subsampling on my data, for several reasons:
- feature selection for the usual motivations (reduce noise, decrease running time, etc.)
- observation subsampling because I have strongly imbalanced data, and I want not to introduce bias towards the most prevalent class in downstream classifiers
My question is: is there a specific order in which I should do feature and observation selection? E.g. first feature selection then subsampling?