Modeling the trade-off between completeness and consistency in genetic-based handwritten character prototyping
Claudio De Stefano, Antonio Della Cioppa, Angelo Marcelli · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
This paper presents a contribution to exploit Learning Classifier Systems using niching for finding the set of prototypes to be used by a Optical Character Recognition system. In particular, we investigate the niching method based on explicit fitness sharing and face the problem of estimating the values of the configuration parameters that allow the system to provide an optimal set of prototypes, i.e. a set of prototypes that represent, for the problem at hand, the best compromise between completeness and consistency. The solution reported in the paper is based on a characterization of the system behavior as a function of the configuration parameters. Such a characterization, derived within the framework set by Learning Classifier System theory, has been used to design an experimental protocol for finding, with a small number of experiments, the desired prototypes. A large set of experiments by using the National Institute of Standards and Technology database has been performed to validate the proposed solution. The experimental findings allow to draw the conclusion that Learning Classifier Systems represent a promising tool to be used for handwritten character recognition.