An Efficient Test of Credal Dominance for the Naive Credal Classifier Defined with Interval Probabilities
Marco Zaffalon · 1999
The naive Bayesian classifier is an effective tool for classification. Recent work has proposed its extension to the treatment of convex sets of probability distributions. The new model can control the variability of the model probabilities, gaining in terms of reliability and flexibility. The classification is obtained through tests of so-called credal dominance. Previous work has provided the procedure for testing credal dominance for general polytopes of distributions, which is based on the solution of linear programs. In this note, the subset of polytopes generated by probability intervals is considered. A specific procedure is provided, for which the linear program can be solved in constant time. The interval case is the proper choice when the classifier is induced from data; therefore the result seems a significant step towards easy and fast implementations of credal classification.