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

arXiv: https://arxiv.org/abs/1709.07377 · HTML: https://mlsanity.com/paper/1709.07377/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2017.

## Built on by (most cited first, 2 of 2)

- [Neural Network Characterization and Entropy Regulated Data Balancing through Principal Component Analysis](https://mlsanity.com/paper/2312.01392/) (2023)
- [Job Offers Classifier using Neural Networks and Oversampling Methods](https://mlsanity.com/paper/2207.06223/) (2022)

## Abstract (arXiv)

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.