# Foundations of data imbalance and solutions for a data democracy

arXiv: https://arxiv.org/abs/2108.00071 · HTML: https://mlsanity.com/paper/2108.00071/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)

## Built on by (most cited first, 11 of 11)

- [Assessing AI Detectors in Identifying AI-Generated Code: Implications for Education](https://mlsanity.com/paper/2401.03676/) (2024)
- [A Survey on AI Assurance](https://mlsanity.com/paper/2111.07505/) (2021)
- [Going Deeper than Tracking: a Survey of Computer-Vision Based Recognition of Animal Pain and Affective States](https://mlsanity.com/paper/2206.08405/) (2022)
- [A Review of Cybersecurity Incidents in the Food and Agriculture Sector](https://mlsanity.com/paper/2403.08036/) (2024)
- [Certified Interpolation Oversampling: Per-Instance Safety Guarantees for Imbalanced Learning](https://mlsanity.com/paper/2501.15790/) (2025)
- [Integration of a machine learning model into a decision support tool to predict absenteeism at work of prospective employees](https://mlsanity.com/paper/2202.03577/) (2022)
- [Experimenting with an Evaluation Framework for Imbalanced Data Learning (EFIDL)](https://mlsanity.com/paper/2301.10888/) (2023)
- [Classification of colorectal primer carcinoma from normal colon with mid-infrared spectra](https://mlsanity.com/paper/2405.10950/) (2024)
- [Leveraging Prompts in LLMs to Overcome Imbalances in Complex Educational Text Data](https://mlsanity.com/paper/2407.01551/) (2024)
- [Neighbor displacement-based enhanced synthetic oversampling for multiclass imbalanced data](https://mlsanity.com/paper/2501.04099/) (2025)
- [Data-Driven Prediction of Maternal Nutritional Status in Ethiopia Using Ensemble Machine Learning Models](https://mlsanity.com/paper/2509.14945/) (2025)

## Abstract (arXiv)

Dealing with imbalanced data is a prevalent problem while performing classification on the datasets. Many times, this problem contributes to bias while making decisions or implementing policies. Thus, it is vital to understand the factors which cause imbalance in the data (or class imbalance). Such hidden biases and imbalances can lead to data tyranny and a major challenge to a data democracy. In this chapter, two essential statistical elements are resolved: the degree of class imbalance and the complexity of the concept; solving such issues helps in building the foundations of a data democracy. Furthermore, statistical measures which are appropriate in these scenarios are discussed and implemented on a real-life dataset (car insurance claims). In the end, popular data-level methods such as random oversampling, random undersampling, synthetic minority oversampling technique, Tomek link, and others are implemented in Python, and their performance is compared.