LoRAS generates synthetic minority samples as convex combinations of multiple Gaussian-perturbed points and claims improved F1 and balanced accuracy over SMOTE variants on 14 datasets.
An approach for classification of highly imbalanced data using weighting and undersampling
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LoRAS: An oversampling approach for imbalanced datasets
LoRAS generates synthetic minority samples as convex combinations of multiple Gaussian-perturbed points and claims improved F1 and balanced accuracy over SMOTE variants on 14 datasets.