Credal discrete classifier

Wenlong Chen, Cyprien Gilet, Benjamin Quost, Sébastien Destercke
Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 290:71-81, 2025.

Abstract

This paper presents a novel extension of the discrete Bayesian classifier (DBC) into a set-valued classification framework grounded in imprecise probability theory. The standard DBC framework, which relies on partitioning the input space into profiles and estimating class-conditional probabilities, may not be very robust to distribution changes or imperfections in observed data. In the hope to mitigate such issues, we introduce the Credal Discrete Classifier (CDC), an imprecise-probabilistic extension of the traditional Bayesian approach. By representing uncertainties in the estimated probabilities through belief functions, CDC offers interval-valued risks and set-valued decisions, thereby enhancing robustness. Experimental results on several benchmark datasets demonstrate that CDC effectively balances accuracy and determinacy by allowing for set-valued predictions in uncertain contexts, often outperforming or matching traditional precise classifiers.

Cite this Paper

BibTeX
@InProceedings{pmlr-v290-chen25a, title = {Credal discrete classifier}, author = {Chen, Wenlong and Gilet, Cyprien and Quost, Benjamin and Destercke, S\'ebastien}, booktitle = {Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {71--81}, year = {2025}, editor = {Destercke, Sébastien and Erreygers, Alexander and Nendel, Max and Riedel, Frank and Troffaes, Matthias C. M.}, volume = {290}, series = {Proceedings of Machine Learning Research}, month = {15--18 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v290/main/assets/chen25a/chen25a.pdf}, url = {https://proceedings.mlr.press/v290/chen25a.html}, abstract = {This paper presents a novel extension of the discrete Bayesian classifier (DBC) into a set-valued classification framework grounded in imprecise probability theory. The standard DBC framework, which relies on partitioning the input space into profiles and estimating class-conditional probabilities, may not be very robust to distribution changes or imperfections in observed data. In the hope to mitigate such issues, we introduce the Credal Discrete Classifier (CDC), an imprecise-probabilistic extension of the traditional Bayesian approach. By representing uncertainties in the estimated probabilities through belief functions, CDC offers interval-valued risks and set-valued decisions, thereby enhancing robustness. Experimental results on several benchmark datasets demonstrate that CDC effectively balances accuracy and determinacy by allowing for set-valued predictions in uncertain contexts, often outperforming or matching traditional precise classifiers.} }
Endnote
%0 Conference Paper %T Credal discrete classifier %A Wenlong Chen %A Cyprien Gilet %A Benjamin Quost %A Sébastien Destercke %B Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2025 %E Sébastien Destercke %E Alexander Erreygers %E Max Nendel %E Frank Riedel %E Matthias C. M. Troffaes %F pmlr-v290-chen25a %I PMLR %P 71--81 %U https://proceedings.mlr.press/v290/chen25a.html %V 290 %X This paper presents a novel extension of the discrete Bayesian classifier (DBC) into a set-valued classification framework grounded in imprecise probability theory. The standard DBC framework, which relies on partitioning the input space into profiles and estimating class-conditional probabilities, may not be very robust to distribution changes or imperfections in observed data. In the hope to mitigate such issues, we introduce the Credal Discrete Classifier (CDC), an imprecise-probabilistic extension of the traditional Bayesian approach. By representing uncertainties in the estimated probabilities through belief functions, CDC offers interval-valued risks and set-valued decisions, thereby enhancing robustness. Experimental results on several benchmark datasets demonstrate that CDC effectively balances accuracy and determinacy by allowing for set-valued predictions in uncertain contexts, often outperforming or matching traditional precise classifiers.
APA
Chen, W., Gilet, C., Quost, B. & Destercke, S.. (2025). Credal discrete classifier. Proceedings of the Fourteenth International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 290:71-81 Available from https://proceedings.mlr.press/v290/chen25a.html.

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