I am building a hierarchical text classifier using the Local Classifier Per Parent Node (LCPN) approach with the 'siblings' policy as described in the PDF:
E.g. if we have the classes 1.1, 1.2, 2.1, 2.2, 2.3 then in the first level we use all the training set to train a classifier to distinguish between class 1 (1.1,1.2) and 2 (2.1,2.2,2.3), at the second level we use two multicalss classifier the first one to classify between 1.1 and 1.2 using as training set only the data belonging to these classes and the second classifer for the rest. Should any data transformation (e.g. scaling, tfidf) that we do to the data happen at each level of the classifier? I.e. since at the first level the tfidf vectors are created by fitting to the whole training set, can we use them at the second level or should we fit to the new training subsets?