Class Imbalance, Redux

By Byron C. Wallace, Kevin Small, Carla E. Brodley, Thomas A. Trikalinos. 2011

227 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Data Augmentation in Emotion Classification Using Generative Adversarial Networks (2017, background): “Some detailed discussions can be referred to [27].”
Striking the Right Balance With Uncertainty (2019, background): “the begininning and not dynamically changed during the course of training. (2) A more stringent caveat is that such costs do not affect the learned boundaries w^ if the training samples are separable [50,23]. Further, when the classes are non-separable, minority class representation in the dataset i”
Machine learning for identifying Randomized Controlled Trials: An evaluation and practitioner's guide (2018, methodology): “For both ML approaches, we used a balanced sampling technique, which aims to improve sensitivity in problems with class imbalance (noting that RCTs form a small minority of all articles).(23,24) In short, multiple training data sets are constructed from the available training data.”
SMOTE for high-dimensional class-imbalanced data (2013, methodology): “Others showed that bagged undersampling techniques outperformed SMOTE for SVM with highdimensional data [19].”
Long-tail learning via logit adjustment (2020, background): “…studied in the literature on learning under class imbalance [Cardie and Howe, 1997, Chawla et al., 2002, Qiao and Liu, 2009, He and Garcia, 2009, Wallace et al., 2011] and the related problem of cost-sensitive learning [Elkan, 2001, Zadrozny and Elkan, 2001, Masnadi-Shirazi and Vasconcelos,…”
Feature-Wise Bias Amplification (2018, background): “The class imbalance problem is a well-studied scenario where some classes in the data are signiﬁcantly less likely than others (Wallace et al., 2011a).”
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification (2021, methodology): “Typical methods include data re-sampling [1, 8, 35, 4, 28], loss re-weighting [26, 7, 30, 33], margin modification [3], and data augmentation [19, 6, 29].”