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pro vyhledávání: '"Pugnana, Andrea"'
Deferring systems extend supervised Machine Learning (ML) models with the possibility to defer predictions to human experts. However, evaluating the impact of a deferring strategy on system accuracy is still an overlooked area. This paper fills this
Externí odkaz:
http://arxiv.org/abs/2405.18902
With the increasing deployment of machine learning models in many socially-sensitive tasks, there is a growing demand for reliable and trustworthy predictions. One way to accomplish these requirements is to allow a model to abstain from making a pred
Externí odkaz:
http://arxiv.org/abs/2401.12708
With the wide adoption of machine learning techniques, requirements have evolved beyond sheer high performance, often requiring models to be trustworthy. A common approach to increase the trustworthiness of such systems is to allow them to refrain fr
Externí odkaz:
http://arxiv.org/abs/2311.09145
Autor:
Pugnana, Andrea, Ruggieri, Salvatore
Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) wit
Externí odkaz:
http://arxiv.org/abs/2210.10703
Publikováno v:
Machine Learning; Aug2024, Vol. 113 Issue 8, p5273-5330, 58p
Akademický článek
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Autor:
Nogueira, Ana Rita1,2 (AUTHOR) ana.r.nogueira@inesctec.pt, Pugnana, Andrea3 (AUTHOR), Ruggieri, Salvatore4 (AUTHOR), Pedreschi, Dino4 (AUTHOR), Gama, João1 (AUTHOR)
Publikováno v:
WIREs: Data Mining & Knowledge Discovery. Mar/Apr2022, Vol. 12 Issue 2, p1-39. 39p.