1.2k citations · 1.3k across the 4 of their papers we have counts for
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cs.LG2017★ 1.2k cited
Oversampling for Imbalanced Learning Based on K-Means and SMOTE
Felix Last, Georgios Douzas, Fernando Bacao
Learning from class-imbalanced data continues to be a common and challenging problem in supervised learning as standard classification algorithms are designed to handle balanced cl…
cs.LG2017★ 11 cited
Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE
Georgios Douzas, Fernando Bacao
Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial da…