Abstract
In this paper we present a new method for fusing classifiers output for problems with a number of classes M > 2. We extend the well-known Behavior Knowledge Space method with a hierarchical approach of the different cells. We propose to add the ranking information of the classifiers output for the combination. Each cell can be divided into new sub-spaces in order to solve ambiguities. We show that this method allows a better control of the rejection, without using new classifiers for the empty cells. This method has been applied on a set of classifiers created by bagging. It has been successfully tested on handwritten character recognition allowing better-detailed results. The technique has been compared with other classical combination methods.
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Cecotti, H., Belaïd, A. (2007). Hierarchical Behavior Knowledge Space. In: Haindl, M., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2007. Lecture Notes in Computer Science, vol 4472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72523-7_42
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DOI: https://doi.org/10.1007/978-3-540-72523-7_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72481-0
Online ISBN: 978-3-540-72523-7
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