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Cluster-based under-sampling approaches for imbalanced data distributions
Expert Systems With Applications, 2009Yue-Shi Lee
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Imbalanced Deep Learning by Minority Class Incremental Rectification
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Qi Dong, Shaogang Gong, Xiatian Zhu
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A survey on imbalanced learning: latest research, applications and future directions
Artificial Intelligence ReviewC L Philip Chen +2 more
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Adaptive multi-objective swarm fusion for imbalanced data classification
Information Fusion, 2018Jinyan Li, Simon Fong, Raymond K Wong
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The effect of imbalanced data sets on LDA: A theoretical and empirical analysis
Pattern Recognition, 2007exaly
Stochastic Covariance Regularization for Imbalanced Datasets
Ahmet Erdem, Faik Boray Tekopenaire +1 more source
Overlapping Classes in Imbalanced Datasets
Big data has become easily available, but there is a need to improve the usefulness of these data, especially when we have an imbalanced dataset and overlapping data points in two or more classes. Machine-learning algorithms have improved in recent years, and many algorithms have been introduced that tackle the issues in data that su er from imbalancedopenaire +1 more source