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 classifiersMy question is: is there a specific order in which I should do feature and observation selection? E.g. first feature selection then subsampling?
Is there a particular order in which to do feature selection and sampling?
feature selection;dimensionality reduction;unbalanced classes;sampling