Oversampling for Imbalanced Learning Based on K-Means and SMOTE

2 code implementations • 2 Nov 2017 • 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 class distributions.

Classification Clustering +2

Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE

1 code implementation • 21 Sep 2017 • Georgios Douzas, Fernando Bacao

While in the basic configuration this region is a hyper-sphere, G-SMOTE allows its deformation to a hyper-spheroid and finally to a line segment, emulating, in the last case, the SMOTE mechanism.