Probabilistic multi-dimensional classification with incomplete data at the prediction time

Thu Ha DO, Vu-Linh Nguyen, Yves Grandvalet
Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, PMLR 300:46-54, 2026.

Abstract

Multi-dimensional classification (MDC) extends multi-class and multi-label learning by predicting several class variables per instance. We revisit probabilistic MDC methods with mixed features (discrete and continuous), focusing on their strengths and limits for handling incomplete data at prediction time. We present theoretical results leading to a new probabilistic approach with efficient learning and prediction algorithms that address scalability and robustness issues. Experiments demonstrate its benefits in different missingness scenarios.

Cite this Paper

BibTeX
@InProceedings{pmlr-v300-do26a, title = { Probabilistic multi-dimensional classification with incomplete data at the prediction time }, author = {DO, Thu Ha and Nguyen, Vu-Linh and Grandvalet, Yves}, booktitle = {Proceedings of The 29th International Conference on Artificial Intelligence and Statistics}, pages = {46--54}, year = {2026}, editor = {Khan, Emtiyaz and Li, Yingzhen and Solin, Arno and Ramdas, Aaditya}, volume = {300}, series = {Proceedings of Machine Learning Research}, month = {02--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v300/main/assets/do26a/do26a.pdf}, url = {https://proceedings.mlr.press/v300/do26a.html}, abstract = { Multi-dimensional classification (MDC) extends multi-class and multi-label learning by predicting several class variables per instance. We revisit probabilistic MDC methods with mixed features (discrete and continuous), focusing on their strengths and limits for handling incomplete data at prediction time. We present theoretical results leading to a new probabilistic approach with efficient learning and prediction algorithms that address scalability and robustness issues. Experiments demonstrate its benefits in different missingness scenarios. } }
Endnote
%0 Conference Paper %T Probabilistic multi-dimensional classification with incomplete data at the prediction time %A Thu Ha DO %A Vu-Linh Nguyen %A Yves Grandvalet %B Proceedings of The 29th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2026 %E Emtiyaz Khan %E Yingzhen Li %E Arno Solin %E Aaditya Ramdas %F pmlr-v300-do26a %I PMLR %P 46--54 %U https://proceedings.mlr.press/v300/do26a.html %V 300 %X Multi-dimensional classification (MDC) extends multi-class and multi-label learning by predicting several class variables per instance. We revisit probabilistic MDC methods with mixed features (discrete and continuous), focusing on their strengths and limits for handling incomplete data at prediction time. We present theoretical results leading to a new probabilistic approach with efficient learning and prediction algorithms that address scalability and robustness issues. Experiments demonstrate its benefits in different missingness scenarios.
APA
DO, T.H., Nguyen, V. & Grandvalet, Y.. (2026). Probabilistic multi-dimensional classification with incomplete data at the prediction time . Proceedings of The 29th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 300:46-54 Available from https://proceedings.mlr.press/v300/do26a.html.

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