Hierarchical behavior knowledge space

Authors:
Hubert Cecotti;Abdel Belaïd
Affiliations:
READ Group , LORIA, CNRS, Vandoeuvre-les-Nancy cedex, France;READ Group , LORIA, CNRS, Vandoeuvre-les-Nancy cedex, France
Venue:
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Year:
2007

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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.