# IMBENS: Ensemble Class-imbalanced Learning in Python

arXiv: https://arxiv.org/abs/2111.12776 · HTML: https://mlsanity.com/paper/2111.12776/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 18 references that are in the map.
- First posted on arXiv 24 Nov 2021.

## Builds on (most cited first, 2 of 2)

- [Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning](https://mlsanity.com/paper/1609.06570/) (2016)
- [Self-paced Ensemble for Highly Imbalanced Massive Data Classification](https://mlsanity.com/paper/1909.03500/) (2019)

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

- [SelfElicit: Your Language Model Secretly Knows Where is the Relevant Evidence](https://mlsanity.com/paper/2502.08767/) (2025)
- [Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting](https://mlsanity.com/paper/2505.18442/) (2025)
- [A Comprehensive Survey on Imbalanced Data Learning](https://mlsanity.com/paper/2502.08960/) (2025)
- [AIM: Attributing, Interpreting, Mitigating Data Unfairness](https://mlsanity.com/paper/2406.08819/) (2024)
- [GraphIFE: Rethinking Graph Imbalance Node Classification via Invariant Learning](https://mlsanity.com/paper/2509.23616/) (2025)
- [Pure Node Selection for Imbalanced Graph Node Classification](https://mlsanity.com/paper/2509.23662/) (2025)

## Abstract (arXiv)

imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem. It provides standard implementations of popular ensemble imbalanced learning (EIL) methods with extended features and utility functions. These ensemble methods include resampling-based, e.g., under/over-sampling, and reweighting-based, e.g., cost-sensitive learning. Beyond the implementation, we empower EIL algorithms with new functionalities like customizable resampling scheduler and verbose logging, thus enabling more flexible training and evaluating strategies. The package was developed under a simple, well-documented API design that follows scikit-learn for increased ease of use. imbens is released under the MIT open-source license and can be installed from Python Package Index (PyPI) or https://github.com/ZhiningLiu1998/imbalanced-ensemble.