Feature Importance - How to choose the number of best features?
This is an important problem, since many feature selection methods return feature scores/importances rather than a finite feature set. I currently know three approaches:


choose the k best attributes (fixed number defined)
choose the best k * 100% of attributes (relative number of features defined)
make a cutoff at the biggest difference in feature scores: all features are ordered according to their score and a split is made at the largest difference between one score and the next lower (biggest loss in importance)


You find a nice implementation doing this in R with the FSelector package.

I think, there are some research works tackling exactly this problem and may suggest better approaches, but I hadn't had the time to go deeper into this.