Model level combination of tree ensemble hyperboxes via GFMM

An ensemble of decision trees defines an overlapping set of hyperboxes. These hyperboxes in turn define a disjoint set of hyperboxes each with an associated vector of individual decisions. These vectors can be used to robustly label the hyperboxes by class, or to define soft labels. We sample from these hyperboxes and use them to build a single classifier within the General Fuzzy Min-Max (GFMM) framework that gains information from many different resamplings of the data through the ensemble from which it is built. This method is found to build robust GFMM models, with improved performance on most datasets compared to the basic GFMM.