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Hierarchical Behavior Knowledge Space

Hubert Cecotti 1 Abdel Belaïd 2 
LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : In this paper we present a new method for fusing classiers output for problems with a number of classes M > 2. We extend the well-known Behavior Knowledge Space method with a hierarchical ap- proach of the dierent cells. We propose to add the ranking information of the classiers 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 classiers for the empty cells. This method has been applied on a set of classi- ers 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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Hubert Cecotti, Abdel Belaïd. Hierarchical Behavior Knowledge Space. 7th International Workshop on Multiple Classier Systems - MCS 2007, May 2007, Prague, Czech Republic. pp.421-430, ⟨10.1007/978-3-540-72523-7_42⟩. ⟨inria-00579636⟩



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