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Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

We provide methods for calculating the robustness of the predictions of two types of generative classifiers whose underlying distribution is a Probabilistic Graphical Model (PGM): naive Bayes classifiers and generative forests (a probabilistic extension of random forests). Following the paradigm of robustness quantification, we define the robustness of a prediction as the extent to which the distribution of the classifier can be perturbed without changing this prediction. We consider perturbations obtained by varying the local models of the PGMs within general neighborhoods and focus in partic

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