Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE
This addresses the challenge of imbalanced learning for machine learning practitioners, offering an incremental enhancement to existing oversampling methods.
The paper tackles the problem of imbalanced dataset classification by proposing Geometric SMOTE (G-SMOTE), a generalization of SMOTE that generates synthetic minority class samples in geometric regions like hyper-spheres or hyper-spheroids, resulting in significant improvement in data quality compared to standard oversampling algorithms.
Classification of imbalanced datasets is a challenging task for standard algorithms. Although many methods exist to address this problem in different ways, generating artificial data for the minority class is a more general approach compared to algorithmic modifications. SMOTE algorithm and its variations generate synthetic samples along a line segment that joins minority class instances. In this paper we propose Geometric SMOTE (G-SMOTE) as a generalization of the SMOTE data generation mechanism. G-SMOTE generates synthetic samples in a geometric region of the input space, around each selected minority instance. 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. The performance of G-SMOTE is compared against multiple standard oversampling algorithms. We present empirical results that show a significant improvement in the quality of the generated data when G-SMOTE is used as an oversampling algorithm.