Context-Specific Refinements of Bayesian Network Classifiers

Manuele Leonelli, Gherardo Varando
Proceedings of The 12th International Conference on Probabilistic Graphical Models, PMLR 246:182-198, 2024.

Abstract

Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy. The widely used naive and TAN classifiers are specific instances of Bayesian network classifiers with a constrained underlying graph. This paper introduces novel classes of generative classifiers extending TAN and other famous types of Bayesian network classifiers. Our approach is based on staged tree models, which extend Bayesian networks by allowing for complex, context-specific patterns of dependence. We formally study the relationship between our novel classes of classifiers and Bayesian networks. We introduce and implement data-driven learning routines for our models and investigate their accuracy in an extensive computational study. The study demonstrates that models embedding asymmetric information can enhance classification accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v246-leonelli24a, title = {Context-Specific Refinements of Bayesian Network Classifiers}, author = {Leonelli, Manuele and Varando, Gherardo}, booktitle = {Proceedings of The 12th International Conference on Probabilistic Graphical Models}, pages = {182--198}, year = {2024}, editor = {Kwisthout, Johan and Renooij, Silja}, volume = {246}, series = {Proceedings of Machine Learning Research}, month = {11--13 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v246/main/assets/leonelli24a/leonelli24a.pdf}, url = {https://proceedings.mlr.press/v246/leonelli24a.html}, abstract = {Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy. The widely used naive and TAN classifiers are specific instances of Bayesian network classifiers with a constrained underlying graph. This paper introduces novel classes of generative classifiers extending TAN and other famous types of Bayesian network classifiers. Our approach is based on staged tree models, which extend Bayesian networks by allowing for complex, context-specific patterns of dependence. We formally study the relationship between our novel classes of classifiers and Bayesian networks. We introduce and implement data-driven learning routines for our models and investigate their accuracy in an extensive computational study. The study demonstrates that models embedding asymmetric information can enhance classification accuracy.} }
Endnote
%0 Conference Paper %T Context-Specific Refinements of Bayesian Network Classifiers %A Manuele Leonelli %A Gherardo Varando %B Proceedings of The 12th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2024 %E Johan Kwisthout %E Silja Renooij %F pmlr-v246-leonelli24a %I PMLR %P 182--198 %U https://proceedings.mlr.press/v246/leonelli24a.html %V 246 %X Supervised classification is one of the most ubiquitous tasks in machine learning. Generative classifiers based on Bayesian networks are often used because of their interpretability and competitive accuracy. The widely used naive and TAN classifiers are specific instances of Bayesian network classifiers with a constrained underlying graph. This paper introduces novel classes of generative classifiers extending TAN and other famous types of Bayesian network classifiers. Our approach is based on staged tree models, which extend Bayesian networks by allowing for complex, context-specific patterns of dependence. We formally study the relationship between our novel classes of classifiers and Bayesian networks. We introduce and implement data-driven learning routines for our models and investigate their accuracy in an extensive computational study. The study demonstrates that models embedding asymmetric information can enhance classification accuracy.
APA
Leonelli, M. & Varando, G.. (2024). Context-Specific Refinements of Bayesian Network Classifiers. Proceedings of The 12th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 246:182-198 Available from https://proceedings.mlr.press/v246/leonelli24a.html.

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