Fusion of Classifiers Based on a Novel 2-Stage Model

The paper introduces a novel 2-Stage model for multi-classifier system. Instead of gathering posterior probabilities resulted from base classifiers into a single dataset called meta-data or Level1 data like in the original 2-Stage model, here we separate data in K Level1 matrices corresponding to the K base classifiers. These data matrices, in turn, are classified in sequence by a new classifier at the second stage to generate output of that new classifier called Level2 data. Next, Weight Matrix algorithm is proposed to combine Level2 data and produces prediction for unlabeled observations. Experimental results on CLEF2009 medical image database demonstrate the benefit of our model in comparison with several existing ensemble learning models.