# Oversampling for Imbalanced Learning Based on K-Means and SMOTE

arXiv: https://arxiv.org/abs/1711.00837 · HTML: https://mlsanity.com/paper/1711.00837/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2017.

## Built on by (most cited first, 30 of 38)

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- [SetConv: A New Approach for Learning from Imbalanced Data](https://mlsanity.com/paper/2104.06313/) (2021)
- [Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification](https://mlsanity.com/paper/2502.06878/) (2025)
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- [Benchmark of Data Preprocessing Methods for Imbalanced Classification](https://mlsanity.com/paper/2303.03094/) (2023)
- [Improving SMOTE via Fusing Conditional VAE for Data-adaptive Noise Filtering](https://mlsanity.com/paper/2405.19757/) (2024)
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- [Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods](https://mlsanity.com/paper/2505.13518/) (2025)
- [RUBRIC: Realism--Utility Balanced Ranking for Imbalanced Classification](https://mlsanity.com/paper/2607.09816/) (2026)
- [Prediction of the Facial Growth Direction is Challenging](https://mlsanity.com/paper/2110.02316/) (2021)
- [Handling Imbalanced Datasets Through Optimum-Path Forest](https://mlsanity.com/paper/2202.08934/) (2022)
- [Evaluating resampling methods on a real-life highly imbalanced online credit card payments dataset](https://mlsanity.com/paper/2206.13152/) (2022)
- [Enhancement Encoding: A Novel Imbalanced Classification Approach via Encoding the Training Labels](https://mlsanity.com/paper/2208.11056/) (2022)
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## Abstract (arXiv)

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. While different strategies exist to tackle this problem, methods which generate artificial data to achieve a balanced class distribution are more versatile than modifications to the classification algorithm. Such techniques, called oversamplers, modify the training data, allowing any classifier to be used with class-imbalanced datasets. Many algorithms have been proposed for this task, but most are complex and tend to generate unnecessary noise. This work presents a simple and effective oversampling method based on k-means clustering and SMOTE oversampling, which avoids the generation of noise and effectively overcomes imbalances between and within classes. Empirical results of extensive experiments with 71 datasets show that training data oversampled with the proposed method improves classification results. Moreover, k-means SMOTE consistently outperforms other popular oversampling methods. An implementation is made available in the python programming language.