Enhancing multiple expert decision combination strategies through exploitation of a priori information sources

The concept of combining multiple experts in a unified framework to generate a combined decision based on individual decisions delivered by the cooperating experts has been exploited in solving the problem of handwritten and machine printed character recognition. The level of performance achieved in terms of the absolute recognition performance and increased confidences associated with these decisions is very encouraging. However, the underlying philosophy behind this success is still not completely understood. The authors analyse the problem of decision combination of multiple experts from a completely different perspective. It is demonstrated that the success or failure of the decision combination strategy largely depends on the extent to which the various possible sources of information are exploited in designing the decision combination framework. Seven different multiple expert decision combination strategies are evaluated in terms of this information management issue. It is demonstrated that it is possible to treat the comparative evaluation of the multiple expert decision combination approaches based on their capability for exploiting diverse information extracted from the various sources as a yardstick in estimating the level of performance that is achievable from these combined configurations