When in Doubt ... Be Indecisive

For a presented case, a Bayesian network classifier in essence computes a posterior probability distribution over its class variable. Based upon this distribution, the classifier’s classification function returns a single, determinate class value and thereby hides the uncertainty involved. To provide reliable decision support, however, the classifier should be able to convey indecisiveness if the posterior distribution computed for the case doesnot clearly favourone classvalue over another. In thispaperwepresent an approach for this purpose, and introduce new measures to capture the performance and practicability of such classifiers.