Rejection methods for an adaptive committee classifier

Adaptation is an effective method for improving classification accuracy and a committee structure can in general improve on its members´ performance. Therefore an adaptive committee structure is a tempting approach. Rejection may be used in handwriting recognition to improve performance through either directing the problematic character to a special classifier that handles such hard cases or discarding it. The experiments in this paper compare several fundamentally different approaches to implementing rejection in an adaptive committee classifier. A dynamically expanding context (DEC) - based committee is used for evaluating these approaches. The results show that if the rejected classes are handled with a 50% error rate, the performance is improved. A scheme in which there is an adjustable threshold for distance-based rejection is an effective method for implementing rejection in this setting