Abstract
Only a few studies have investigated on how to select component classifiers from a classifier pool. But, the performance of multiple classifier systems depends on the component classifiers as well as the combination methods. A couple of information-theoretic methods selecting the component classifiers by considering the relationship among classifiers are proposed in this paper. These methods are applied to the classifier pool and examine the possible classifier sets for building the multiple classifier systems. A classifier set is selected as a candidate and evaluated with the other classifier sets on the recognition of unconstrained handwritten numerals.
This research was financially supported by Hansung University in the year of 2005.
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Kang, HJ., Choo, M. (2005). Information-Theoretic Selection of Classifiers for Building Multiple Classifier Systems. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_94
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DOI: https://doi.org/10.1007/11538059_94
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28226-6
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