# CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification

arXiv: https://arxiv.org/abs/2004.03409 · HTML: https://mlsanity.com/paper/2004.03409/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 6 of 6)

- [RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification](https://mlsanity.com/paper/2105.04009/) (2021)
- [Automated Imbalanced Learning](https://mlsanity.com/paper/2211.00376/) (2022)
- [Potential Anchoring for imbalanced data classification](https://mlsanity.com/paper/2104.08548/) (2021)
- [Imbalanced data preprocessing techniques utilizing local data characteristics](https://mlsanity.com/paper/2111.14120/) (2021)
- [Neural Network Architecture for Database Augmentation Using Shared Features](https://mlsanity.com/paper/2302.01374/) (2023)
- [Skew-Probabilistic Neural Networks for Learning from Imbalanced Data](https://mlsanity.com/paper/2312.05878/) (2023)

## Abstract (arXiv)

In this paper we propose a novel data-level algorithm for handling data imbalance in the classification task, Synthetic Majority Undersampling Technique (SMUTE). SMUTE leverages the concept of interpolation of nearby instances, previously introduced in the oversampling setting in SMOTE. Furthermore, we combine both in the Combined Synthetic Oversampling and Undersampling Technique (CSMOUTE), which integrates SMOTE oversampling with SMUTE undersampling. The results of the conducted experimental study demonstrate the usefulness of both the SMUTE and the CSMOUTE algorithms, especially when combined with more complex classifiers, namely MLP and SVM, and when applied on datasets consisting of a large number of outliers. This leads us to a conclusion that the proposed approach shows promise for further extensions accommodating local data characteristics, a direction discussed in more detail in the paper.