Investigation of a novel self-configurable multiple classifier system for character recognition

In this paper we introduce a global optimisation technique, namely a genetic algorithm, into a parallel multiclassifier system design process. As few similar systems have been proposed to date our main focus in this study is to explore the statistical properties of the self-configuration process in order to enhance our understanding of its internal operational mechanism and to propose possible improvements. For this we tested our system in a series of character recognition tasks ranging from printed to handwritten data. Subsequently, we compare its performance with that of two alternative multiple classifier combination strategies. Finally, we investigate, over a set of cross-validating experiments, the relation between the performances of the individual classifiers and their variability, and the frequency with which each of them is chosen to participate in the final configuration generated by the genetic algorithm