Yes, I worked on quite a few boosting / bagging / random forests classifiers. The key thing I learned, though, was that it wasn't possible to take the _entire_

Yes, I worked on quite a few boosting / bagging / random forests classifiers. The key thing I learned, though, was that it wasn't possible to take the _entire_ dataset for some problem, throw even an ensemble method at it, and expect high detection and low FP rates. As you noted, one has to filter and prune using domain knowledge or hand-verified heuristics before you'd see decent performance.