Learning from Imbalanced Data Sets

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Imbalance Problems in Object Detection: A Review (2019, background): “Another set of relevant work includes the studies specifically for imbalance problems in machine learning [40], [41], [42], [43].”
Class-Weighted Classification: Trade-offs and Robust Approaches (2020, background): “We briefly review other research related to imbalanced classification, but for a far more exhaustive treatment, see a survey of the area (He and Garcia, 2009; Fernández et al., 2018).”
How Complex Is Your Classification Problem? (2018, methodology): “t al. [2013], Anwar et al. [2014], Santos et al. [2018] and Zhang et al. [2019]. More discussions on the effectiveness of data complexity analysis related to the data imbalance theme can be found in [Fernández et al., 2018]. 4.3 Learning Algorithms Data complexity measures can also be employed for a”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “In a class-imbalanced dataset, one of its classes has a sig-niﬁcantly lower number of samples than the other [1].”
Multi-head CNN-RNN for multi-time series anomaly detection: An industrial case study (2019, background): “The imbalanced data problem [22] is also a common issue in anomaly detection scenarios since usually there are very few anomalous observations available and large volumes of normal observations.”