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

Adrián Detavernier, Jasper De Bock
Proceedings of the 13th International Conference on Probabilistic Graphical Models, PMLR 343:306-322, 2026.

Abstract

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 particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.

Cite this Paper

BibTeX
@InProceedings{pmlr-v343-detavernier26a, title = {Local Robustness Quantification for Naive {Bayes} Classifiers and Generative Forests: a General Approach}, author = {Detavernier, Adri\'an and De Bock, Jasper}, booktitle = {Proceedings of the 13th International Conference on Probabilistic Graphical Models}, pages = {306--322}, year = {2026}, editor = {Camps-Valls, Gustau and Leonelli, Manuele and Varando, Gherardo}, volume = {343}, series = {Proceedings of Machine Learning Research}, month = {09--11 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v343/main/assets/detavernier26a/detavernier26a.pdf}, url = {https://proceedings.mlr.press/v343/detavernier26a.html}, abstract = {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 particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.} }
Endnote
%0 Conference Paper %T Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach %A Adrián Detavernier %A Jasper De Bock %B Proceedings of the 13th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2026 %E Gustau Camps-Valls %E Manuele Leonelli %E Gherardo Varando %F pmlr-v343-detavernier26a %I PMLR %P 306--322 %U https://proceedings.mlr.press/v343/detavernier26a.html %V 343 %X 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 particular on epsilon-contamination, total variation distance and chi-squared divergence balls. We test our methods on benchmark datasets, demonstrate that the robustness value of a prediction serves as an indicator for its trustworthiness and compare our approach with other such indicators.
APA
Detavernier, A. & De Bock, J.. (2026). Local Robustness Quantification for Naive Bayes Classifiers and Generative Forests: a General Approach. Proceedings of the 13th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 343:306-322 Available from https://proceedings.mlr.press/v343/detavernier26a.html.

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