Reviews: Pruning Random Forests for Prediction on a Budget

The idea of taking into account feature costs when pruning tree ensembles is original to the best of my knowledge. The main originality of the proposed approach is the fact that it adopts a bottom-up post-pruning strategy, while most existing approaches are top-down, acting during tree growing. While the authors present this feature as an advantage of their method, actually, I'm not convinced that adopting a bottom-up strategy is a good idea for addressing this problem. Since the algorithm indeed can not modify the existing tree structure (it can only prune it), it should be less efficient in terms of feature cost reduction than top-down methods that can have a direct impact on the features selected at tree nodes. For example, let us assume that two very important features in the dataset carry on the exact same information about the output (i.e, they are redundant).