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This paper considers the problem of removing costly features from a Bayesiannetwork classifier. We want the classifier to be robust to these changes, andmaintain its classification behavior. To this end, we propose a closenessmetric between Bayesian classifiers, called the expected classificationagreement (ECA). Our corresponding trimming algorithm finds an optimal subsetof features and a new classification threshold that maximize the expectedagreement, subject to a budgetary constraint. It utilizes new theoreticalinsights to perform branch-and-bound search in the space of feature sets, whilecomputing bounds on the ECA. Our experiments investigate both the runtime costof trimming and its effect on the robustness and accuracy of the finalclassifier.

On Robust Trimming of Bayesian Network Classifiers