Categorization of approaches to deal with imbalanced classes
The way I see it all three categorizations agree in many things. For example, all three have a category for pre-processing steps.

I would tend to mostly agree on the third categorization as its more generic and encompasses more things.


The data-level category includes any pre-processing steps dealing with class imbalance (e.g. over/under sampling).
The algorithm-level could be considered to include the second categories of the first two articles. Any change to the algorithm that deals with class imbalance would go here (e.g. class weighting). 
Finally, a hybrid category for combining the two.


The only thing missing from the first two articles are the post-processing steps, which to be honest, aren't used in practice as often as the other.