Abstract
AbstractPattern recognition is playing an increasingly important role in chemical and biochemical data analysis. Many of these pattern recognition applications call for the discrimination of more than two classes of objects. Decision pathway modeling is proposed as a novel pattern recognition technique for multigroup classification. Decision pathway modeling decomposes the multigroup classification problem into simpler binary discrimination tasks, which are then reassembled into a single hierarchical architecture. To minimize effects of error propagation through the hierarchical architecture, dynamic pathway selection is proposed to adaptively direct the classification of new samples. Decision pathway modeling is compared against generalized multigroup and coupled binary discriminant techniques in terms of classification accuracy. The benefit of decision pathway modeling is shown to arise from the hierarchical decomposition and by the dynamic selection of classification pathways. Copyright © 2004 John Wiley & Sons, Ltd.
Published Version
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