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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Cecotti, H., & Belaïd, A. (2007). Hierarchical behavior knowledge space. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4472 LNCS, pp. 421–430). Springer Verlag. https://doi.org/10.1007/978-3-540-72523-7_42
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