A drop-out mechanism for active learning based on one-attribute heuristics

Active Learning (AL) leverages the principle that machine learning models can achieve high accuracy with fewer labeled samples by strategically selecting the most informative data points for training. However, when human annotators provide these labels, their decisions might exhibit a systematic bia...

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Auteurs principaux:	Sriram Ravichandran, Nandan Sudarsanam, Balaraman Ravindran, Konstantinos V. Katsikopoulos
Format:	Artigo
Langue:	Inglês
Publié:	Frontiers Media S.A. 2025-09-01
Collection:	Frontiers in Artificial Intelligence
Sujets:	active learning human-in-the loop human behavior biases fast and frugal heuristics
Accès en ligne:	https://www.frontiersin.org/articles/10.3389/frai.2025.1562916/full
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