Hierarchical classifier with overlapping class groups

doi:10.1016/j.eswa.2006.10.007

Hierarchical classifier with overlapping class groups

2008-01-01

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Igor T. Podolak *

DOI：10.1016/j.eswa.2006.10.007

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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. (c) 2006 Elsevier Ltd. All rights reserved.

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classifier neural networks

Hierarchical classifier with overlapping class groups

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