Conditional Allocation and Stopping Rules in Bayesian Pattern Recognition

This paper considers the problem of stopping rules, in the context of sequential Bayesian classification. In particular a new criterion, based on the probability of reversal of the obtained classification, is introduced and compared to more commonly used strategies. The results show good behavior of the proposed technique, with both simulated and real data drawn from biomedical application. In fact it appears that this stopping rule reduces the misallocation error rate with the same mean number of used features, or conversely, with an equal level of misallocation error rate, it reduces the mean number of features necessary to attain it.