Abstract
Artificial Intelligence (AI) methods are used to build classifiers that give different levels of accuracy and solution explication. The intent of this paper is to provide a way of building a hierarchical classifier composed of several artificial neural networks (ANN’s) organised in a tree-like fashion. Such method of construction allows for partition of the original problem into several sub-problems which can be solved with simpler ANN’s, and be built quicker than a single ANN. As the sub-problems extracted start to be independent of one another, this paves a way to realise the solutions for the individual sub-problems in a parallel fashion. It is observed that incorrect classifications are not random and can be therefore used to find clusters defining sub-problems.
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© 2006 Springer-Verlag Berlin Heidelberg
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Podolak, I.T., Biel, S., Bobrowski, M. (2006). Hierarchical Classifier. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_71
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DOI: https://doi.org/10.1007/11752578_71
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34141-3
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