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arXiv · 2004.10468

SoQal: Selective Oracle Questioning in Active Learning

Dani Kiyasseh·Tingting Zhu·David A. Clifton
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Abstract

Large sets of unlabelled data within the healthcare domain remain underutilized. Active learning offers a way to exploit these datasets by iteratively requesting an oracle (e.g. medical professional) to label instances. This process, which can be costly and time-consuming is overly-dependent upon an oracle. To alleviate this burden, we propose SoQal, a questioning strategy that dynamically determines when a label should be requested from an oracle. We perform experiments on five publically-available datasets and illustrate SoQal's superiority relative to baseline approaches, including its ability to reduce oracle label requests by up to 35%. SoQal also performs competitively in the presence of label noise: a scenario that simulates clinicians' uncertain diagnoses when faced with difficult classification tasks.

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Dani Kiyasseh, Tingting Zhu, David A. Clifton. 2020-04-22. SoQal: Selective Oracle Questioning in Active Learning. https://arxiv.org/abs/2004.10468

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