Hierarchical classifier with overlapping class groups

In this paper a novel complex classifier architecture is proposed. The architecture has a hierarchical tree-like structure with simple artificial neural networks (ANNs) at each node. The actual structure for a given problem is not preset but is built throughout training.The training algorithm’s ability to build the tree-like structure is based on the assumption that when a weak classifier (i.e., one that classifies only slightly better than a random classifier) is trained and examples from any two output classes are frequently mismatched, then they must carry similar information and constitute a sub-problem. After each ANN has been trained its incorrect classifications are analyzed and new sub-problems are formed. Consequently, new ANNs are built for each of these sub-problems and form another layer of the hierarchical classifier.An important feature of the hierarchical classifier proposed in this work is that the problem partition forms overlapping sub-problems. Thus, the classification follows not just a single path from the root, but may fork enhancing the power of the classification. It is shown how to combine the results of these individual classifiers.