Gene expression Detecting potential labeling errors in microarrays by data perturbation

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Learning kernel logistic regression in the presence of class label noise (2014, background): “The presence of class label noise inherent in training samples has been reported to deteriorate the performance of the existing classifiers in a broad range of classification problems including biomedical data analysis [20,30] and image classification [24,47].”
Deep learning with noisy labels: exploring techniques and remedies in medical image analysis (2019, background): “For example, [51], [52] propose to detect mislabeled instances based on their impact on the classification of other instances in a leave-one-out framework.”
Classification in the Presence of Label Noise: A Survey (2014, background): “For example, Malossini et al. [53] deﬁne the leave-one-out perturbed classiﬁcation (LOOPC) matrix, where the ( i , j ) entry is the label predicted for the j th training sample if: 1) the j th sample itself is removed from the training set and 2) the label of the i th sample is ﬂipped.”
A Light CNN for Deep Face Representation With Noisy Labels (2015, background): “The second category [22, 40] aims to improve the quality of training data by identifying mislabeled instances.”
Label-Noise Robust Logistic Regression and Its Applications (2012, background): “[21] ◦ ◦ ◦ ◦ ◦ ◦ ◦ T8,N2,N28,N29 reg-rLR by frequency 0.”