A feedback-based multi-classifier system

Multi-classifier approach is a widespread strategy used in many difficult classification problems. Traditionally, in a multi-classifier approach, a classification decision based on the combination of a multitude of classifiers is expected to outperform the decisions of each individual classifier. Therefore, in a multi-classifier systems, the potential of the whole set of classifiers is only exploited at the level of the final decision, in which the contributions of all classifiers is used by combining their individual decisions. This paper shows a feed-back based multi-classifier system in which the multi-classifier approach is used not only for providing the final decision, but also for improving the performance of the individual classifiers, by means of a closed-loop strategy. The experimental tests have been carried out in the field of hand-written numeral recognition. The result demonstrates the effectiveness of the proposed approach and its superiority with respect to traditional approach.