Optimizing class-related thresholds with particle swarm optimization

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 tradeoff better than a single global threshold. In this work we argue that the error-reject tradeoff 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.