Trainable Multiple Classifier Schemes for Handwritten Character Recognition
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Exploiting Reliability for Dynamic Selection of Classifiers by Means of Genetic Algorithms
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
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Abstract: In this paper we introduce a global optimisation technique, namely a genetic algorithm, into a parallel multi-classifier 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.