Efficient Multi-Label Conformal Classification

Henrik Boström, Ulf Norinder
Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, PMLR 329:750-760, 2026.

Abstract

In multi-label classification, the label space consists of the power set of a given set of class labels. Generating conformal classifiers for such tasks may be challenging, as the label space grows exponentially with the number of class labels, potentially resulting in a very high computational cost. Moreover, prediction sets containing label sets may be difficult to interpret; for example, the inclusion of a superset does not entail the inclusion of any of its subsets. A novel approach to multi-label conformal classification is proposed that addresses these problems by assuming an object-specific scoring function over the class labels, defining an ordering according to which labels are included in the prediction set. Results from an empirical investigation on six multi-label classification datasets, using both multiple single-target models and single multiple-target models, confirm that the theoretically guaranteed coverage is achieved and show that the choice of scoring function may have a substantial impact on predictive efficiency.

Cite this Paper

BibTeX
@InProceedings{pmlr-v329-bostrom26c, title = {Efficient Multi-Label Conformal Classification}, author = {Bostr{\"o}m, Henrik and Norinder, Ulf}, booktitle = {Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications}, pages = {750--760}, year = {2026}, editor = {Ahlberg, Ernst and Johansson, Ulf and Boström, Henrik and Carlevaro, Alberto and Hallberg Szabadváry, Johan and Carlsson, Lars}, volume = {329}, series = {Proceedings of Machine Learning Research}, month = {02--04 Sep}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v329/main/assets/bostrom26c/bostrom26c.pdf}, url = {https://proceedings.mlr.press/v329/bostrom26c.html}, abstract = {In multi-label classification, the label space consists of the power set of a given set of class labels. Generating conformal classifiers for such tasks may be challenging, as the label space grows exponentially with the number of class labels, potentially resulting in a very high computational cost. Moreover, prediction sets containing label sets may be difficult to interpret; for example, the inclusion of a superset does not entail the inclusion of any of its subsets. A novel approach to multi-label conformal classification is proposed that addresses these problems by assuming an object-specific scoring function over the class labels, defining an ordering according to which labels are included in the prediction set. Results from an empirical investigation on six multi-label classification datasets, using both multiple single-target models and single multiple-target models, confirm that the theoretically guaranteed coverage is achieved and show that the choice of scoring function may have a substantial impact on predictive efficiency.} }
Endnote
%0 Conference Paper %T Efficient Multi-Label Conformal Classification %A Henrik Boström %A Ulf Norinder %B Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications %C Proceedings of Machine Learning Research %D 2026 %E Ernst Ahlberg %E Ulf Johansson %E Henrik Boström %E Alberto Carlevaro %E Johan Hallberg Szabadváry %E Lars Carlsson %F pmlr-v329-bostrom26c %I PMLR %P 750--760 %U https://proceedings.mlr.press/v329/bostrom26c.html %V 329 %X In multi-label classification, the label space consists of the power set of a given set of class labels. Generating conformal classifiers for such tasks may be challenging, as the label space grows exponentially with the number of class labels, potentially resulting in a very high computational cost. Moreover, prediction sets containing label sets may be difficult to interpret; for example, the inclusion of a superset does not entail the inclusion of any of its subsets. A novel approach to multi-label conformal classification is proposed that addresses these problems by assuming an object-specific scoring function over the class labels, defining an ordering according to which labels are included in the prediction set. Results from an empirical investigation on six multi-label classification datasets, using both multiple single-target models and single multiple-target models, confirm that the theoretically guaranteed coverage is achieved and show that the choice of scoring function may have a substantial impact on predictive efficiency.
APA
Boström, H. & Norinder, U.. (2026). Efficient Multi-Label Conformal Classification. Proceedings of the Fifteenth Symposium on Conformal and Probabilistic Prediction with Applications, in Proceedings of Machine Learning Research 329:750-760 Available from https://proceedings.mlr.press/v329/bostrom26c.html.

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