A Novel Confidence-Based Framework for Multiple Expert Decision Fusion

A novel confidence-based parallel multiple expert decision combination framework is introduced. The traditional approaches to parallel multiple source decision fusion either take no account of the confidence of each decision by each participating expert in the combined framework or only exploit the confidences associated with each decision. But it is entirely possible to incorporate more additional a priori information in the form of various confidence indices that can be estimated from the performances of various participating experts. The confidence-based parallel multiple expert decision combination framework proposed here addresses this shortcoming. Very encouraging results have been obtained by implementing this proposed framework in combining decisions of multiple experts applied to the problem of handwritten and machine printed character recognition.