{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/learning-sets-of-probabilities-through","title":"Learning Sets of Probabilities Through Ensemble Methods","arxiv_id":null,"date":"2023-11-19","proceeding":"ECSQARU 2023 2023 11","authors":["Vu-Linh Nguyen","Haifei Zhang","Sébastien Destercke"],"abstract":"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 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 the 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.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-45608-4_21","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-031-45608-4_21","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"learning-sets-of-probabilities-through","repo_url":"https://github.com/Haifei-ZHANG/Probability-Sets-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[{"method_slug":"set","method_name":"SET"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}