Article Dans Une Revue Machine Learning Année : 2025

Credal ensembling in multi-class classification

Résumé

In this paper, we present a formal framework to (1) aggregate probabilistic ensemble members into either a representative classifier or a credal classifier, and (2) perform various decision tasks based on this uncertainty quantification. We first elaborate on the aggregation problem under a class of distances between distributions. We then propose generic methods to robustify uncertainty quantification and decisions, based on the obtained ensemble and representative probability. To facilitate the scalability of the proposed framework, for all the problems and applications covered, we elaborate on their computational complexities from the theoretical aspects and leverage theoretical results to derive efficient algorithmic solutions. Finally, relevant sets of experiments are conducted to assess the usefulness of the proposed framework in uncertainty sampling, classification with a reject option, and set-valued prediction-making.
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hal-04917279 , version 1 (28-01-2025)

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Vu-Linh Nguyen, Haifei Zhang, Sébastien Destercke. Credal ensembling in multi-class classification. Machine Learning, 2025, 114 (1), pp.19. ⟨10.1007/s10994-024-06703-y⟩. ⟨hal-04917279⟩
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