In the present paper we propose a method for determining the best trade-off between error rate and reject rate for a multi-expert system (MES) using the Bayesian combining rule. The method is based on the estimation of the reliability of each classification act and on the evaluation of the convenience of rejecting the input sample when the reliability is under a threshold, evaluated on the basis of the requirements of the application domain. The adaptability to the given domain represents an original feature since, till now, the problem of defining a reject rule for an MES has not been systematically introduced, and the few existing proposals seldom take into account the requirements of the domain. The method has been widely tested with reference to the recognition of handwritten characters coming from a standard database. The results are also compared with those provided by employing the well-known Chow's rule.
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