# Radial-Based Undersampling for Imbalanced Data Classification

arXiv: https://arxiv.org/abs/1906.00452 · HTML: https://mlsanity.com/paper/1906.00452/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2019.

## Built on by (most cited first, 22 of 22)

- [DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data](https://mlsanity.com/paper/2105.02340/) (2021)
- [ProCo: Prototype-aware Contrastive Learning for Long-tailed Medical Image Classification](https://mlsanity.com/paper/2209.00183/) (2022)
- [Large-scale Long-tailed Disease Diagnosis on Radiology Images](https://mlsanity.com/paper/2312.16151/) (2023)
- [DBN-Mix: Training Dual Branch Network Using Bilateral Mixup Augmentation for Long-Tailed Visual Recognition](https://mlsanity.com/paper/2207.02173/) (2022)
- [RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification](https://mlsanity.com/paper/2105.04009/) (2021)
- [An Asymmetric Contrastive Loss for Handling Imbalanced Datasets](https://mlsanity.com/paper/2207.07080/) (2022)
- [Automated Imbalanced Learning](https://mlsanity.com/paper/2211.00376/) (2022)
- [Potential Anchoring for imbalanced data classification](https://mlsanity.com/paper/2104.08548/) (2021)
- [Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue](https://mlsanity.com/paper/2203.11410/) (2022)
- [Few-shot 1/a Anomalies Feedback : Damage Vision Mining Opportunity and Embedding Feature Imbalance](https://mlsanity.com/paper/2307.12676/) (2023)
- [A Review of Machine Learning Techniques in Imbalanced Data and Future Trends](https://mlsanity.com/paper/2310.07917/) (2023)
- [MCRAGE: Synthetic Healthcare Data for Fairness](https://mlsanity.com/paper/2310.18430/) (2023)
- [Rebalancing the Scales: A Systematic Mapping Study of Generative Adversarial Networks (GANs) in Addressing Data Imbalance](https://mlsanity.com/paper/2502.16535/) (2025)
- [Hybrid Imbalanced Regression Through Unified Data-Level and Algorithm-Level Balancing](https://mlsanity.com/paper/2606.01221/) (2026)
- [Imbalanced data preprocessing techniques utilizing local data characteristics](https://mlsanity.com/paper/2111.14120/) (2021)
- [Handling Imbalanced Datasets Through Optimum-Path Forest](https://mlsanity.com/paper/2202.08934/) (2022)
- [MIGT: Memory Instance Gated Transformer Framework for Financial Portfolio Management](https://mlsanity.com/paper/2502.07280/) (2025)
- [A Novel Double Pruning method for Imbalanced Data using Information Entropy and Roulette Wheel Selection for Breast Cancer Diagnosis](https://mlsanity.com/paper/2503.12239/) (2025)
- [Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning](https://mlsanity.com/paper/2503.18569/) (2025)
- [Beyond Imbalance Ratio: Data Characteristics as Critical Moderators of Oversampling Method Selection](https://mlsanity.com/paper/2604.04541/) (2026)
- [DADIR: Density-Aware Data-level Imbalanced Regression Framework](https://mlsanity.com/paper/2607.17178/) (2026)
- [GERIS: A Game-Theoretic Framework for Filtering Instance-Dependent Label Noise in License Plate Data Augmentation](https://mlsanity.com/paper/2609.31731/) (2026)

## Abstract (arXiv)

Data imbalance remains one of the most widespread problems affecting contemporary machine learning. The negative effect data imbalance can have on the traditional learning algorithms is most severe in combination with other dataset difficulty factors, such as small disjuncts, presence of outliers and insufficient number of training observations. Aforementioned difficulty factors can also limit the applicability of some of the methods of dealing with data imbalance, in particular the neighborhood-based oversampling algorithms based on SMOTE. Radial-Based Oversampling (RBO) was previously proposed to mitigate some of the limitations of the neighborhood-based methods. In this paper we examine the possibility of utilizing the concept of mutual class potential, used to guide the oversampling process in RBO, in the undersampling procedure. Conducted computational complexity analysis indicates a significantly reduced time complexity of the proposed Radial-Based Undersampling algorithm, and the results of the performed experimental study indicate its usefulness, especially on difficult datasets.