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Beigman and Beigman [4], Guan et al [7], Lee et al [8], Choi et al [9], and Sukhbaatar and Fergus [10] attempted to develop models from noisy datasets directly. Others such as Brodley and Friedl [11] identified and reduced noisy data using majority voting before training. This research claims that they can make a model robust for up to 30% of label noise. This type of research is subject to the risk of classifying hard labels as noisy labels.
JMIR Med Inform 2020;8(8):e18089
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