Optimizing class-related thresholds with Particle Swarm Optimization

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Optimizing class-related thresholds with Particle Swarm Optimization

Oliveira, Luiz S. and Britto, Alceu S. and Sabourin, Robert

Proceedings of the International Joint Conference on Neural Networks 2005

Abstract : In this paper we address the issue of class-related reject thresholds for classification systems. It has been demonstrated in the literature that class-related reject thresholds provide an error-reject trade-off better than a single global threshold. In this work we argue that the error-reject trade-off yielded by class-related reject thresholds can be further improved if a proper algorithm is used to find the thresholds. In light of this, we propose using a recently developed optimization algorithm called Particle Swarm Optimization. It has been proved to be very effective in solving real valued global optimization problems. In order to show the benefits of such an algorithm, we have applied it to optimize the thresholds of a cascading classifier system devoted to recognize handwritten digits. © 2005 IEEE.

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