# Potential Anchoring for imbalanced data classification

arXiv: https://arxiv.org/abs/2104.08548 · HTML: https://mlsanity.com/paper/2104.08548/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 59 references that are in the map.
- First posted on arXiv 17 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [Radial-Based Undersampling for Imbalanced Data Classification](https://mlsanity.com/paper/1906.00452/) (2019)
- [CSMOUTE: Combined Synthetic Oversampling and Undersampling Technique for Imbalanced Data Classification](https://mlsanity.com/paper/2004.03409/) (2020)

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

- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [Imbalanced data preprocessing techniques utilizing local data characteristics](https://mlsanity.com/paper/2111.14120/) (2021)

## Abstract (arXiv)

Data imbalance remains one of the factors negatively affecting the performance of contemporary machine learning algorithms. One of the most common approaches to reducing the negative impact of data imbalance is preprocessing the original dataset with data-level strategies. In this paper we propose a unified framework for imbalanced data over- and undersampling. The proposed approach utilizes radial basis functions to preserve the original shape of the underlying class distributions during the resampling process. This is done by optimizing the positions of generated synthetic observations with respect to the potential resemblance loss. The final Potential Anchoring algorithm combines over- and undersampling within the proposed framework. The results of the experiments conducted on 60 imbalanced datasets show outperformance of Potential Anchoring over state-of-the-art resampling algorithms, including previously proposed methods that utilize radial basis functions to model class potential. Furthermore, the results of the analysis based on the proposed data complexity index show that Potential Anchoring is particularly well suited for handling naturally complex (i.e. not affected by the presence of noise) datasets.