Title: Geometric SMOTE: Effective oversampling for imbalanced learning through a geometric extension of SMOTE,
Abstract: 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.