Reliable classification: learning classifiers that distinguish aleatoric and epistemic uncertainty

DOI10.1016/j.ins.2013.07.030zbMath1320.68145OpenAlexW2006715371MaRDI QIDQ497886

Robin Senge, Norbert Donner-Banzhoff, Eyke Hüllermeier, Jörg Haasenritter, Oliver Hirsch, Stefan Bösner, Krzysztof Dembczyński

Publication date: 25 September 2015

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2013.07.030

zbMATH Keywords

classificationuncertaintymedical diagnosismachine learningmedical decision making

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Medical applications (general) (92C50) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (9)

How to measure uncertainty in uncertainty sampling for active learning ⋮ Relative entropy collaborative fuzzy clustering method ⋮ Extremely randomized neural networks for constructing prediction intervals ⋮ Responsible model deployment via model-agnostic uncertainty learning ⋮ Evidence-based uncertainty sampling for active learning ⋮ Efficient set-valued prediction in multi-class classification ⋮ Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods ⋮ Cautious classification based on belief functions theory and imprecise relabelling ⋮ Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression

Uses Software

• UCI-ml
• PRMLT

Cites Work

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• Default reasoning and possibility theory
• A semantics for possibility theory based on likelihoods
• Triangular norms as ordinal sums of semigroups in the sense of A. H. Clifford
• Belief functions and statistical inference

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