# Kernel-Based Enhanced Oversampling Method for Imbalanced Classification

arXiv: https://arxiv.org/abs/2504.09147 · HTML: https://mlsanity.com/paper/2504.09147/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 21 references that are in the map.
- First posted on arXiv 12 Apr 2025.

## Builds on (most cited first, 2 of 2)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Review of Methods for Handling Class-Imbalanced in Classification Problems](https://mlsanity.com/paper/2211.05456/) (2022)

## Abstract (arXiv)

This paper introduces a novel oversampling technique designed to improve classification performance on imbalanced datasets. The proposed method enhances the traditional SMOTE algorithm by incorporating convex combination and kernel-based weighting to generate synthetic samples that better represent the minority class. Through experiments on multiple real-world datasets, we demonstrate that the new technique outperforms existing methods in terms of F1-score, G-mean, and AUC, providing a robust solution for handling imbalanced datasets in classification tasks.