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ESWA
2008

Hierarchical classifier with overlapping class groups

13 years 12 months ago
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 clas...
Igor T. Podolak
Added 10 Dec 2010
Updated 10 Dec 2010
Type Journal
Year 2008
Where ESWA
Authors Igor T. Podolak
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