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Communication Dans Un Congrès Année : 2023

Learning Sets of Probabilities Through Ensemble Methods

Résumé

A possible approach to obtain set-valued predictions is to learn for each query instance a probability set (a.k.a. credal set) representing its associated uncertainty. Theoretically founded decision rules extending classical expectation and inducing a partial order between predictions can the be used to derive set-valued predictions. However, obtaining such a credal set by imprecisiating a given learning algorithm is usually computationally challenging, except for simple models such as decision trees or naive Bayes classifiers. In this paper, we propose a simple, easy to use quantile-based framework for estimating credal sets using output of ensemble methods, that can also cope with complex types of data, such as images and mixed/multimodal data, etc. Experiments are conducted to highlight the usefulness of the proposed framework.
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Dates et versions

hal-04371410 , version 1 (03-01-2024)

Identifiants

Citer

Vu-Linh Nguyen, Haifei Zhang, Sébastien Destercke. Learning Sets of Probabilities Through Ensemble Methods. 17th European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU 2023), Sep 2023, Arras, France. pp.270-283, ⟨10.1007/978-3-031-45608-4_21⟩. ⟨hal-04371410⟩
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