Hi, I am using a machine learning code written by somebody else where they run the first level of classification using 10 features. Then they select some instances which were not classified with certainty (between a certain range of output value) and rerun them again with a smaller subset of those 10 features but different classifiers not used before (eg., SVM but different model of it). Then they do this one more time with another subset and different classification models. So, some of these features are used in 3 different rounds. What are the pitfalls of this approach, if any? Is this over-fitting in any way (not in a traditional sense of course). Will this approach be criticized if we launched with this?