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imbalanced-learn-extra
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Resumo(s)
Learning from imbalanced data is a common challenge in supervised learning, as most classifiers assume balanced class distributions. Among the strategies to mitigate this issue, oversampling algorithms offer a flexible and model-agnostic solution by generating synthetic samples for minority classes. In this paper, we introduce the imbalanced-learn-extra Python library, an open-source extension of the imbalanced-learn ecosystem that provides additional oversampling techniques for research and practical use. The library integrates seamlessly with Scikit-Learn, allowing users to easily incorporate it into existing workflows. It implements Geometric SMOTE, a geometrically enhanced drop-in replacement for the original SMOTE algorithm, and clustering-based oversampling methods such as KMeans-SMOTE and G-SOMO, which combine existing imbalanced-learn oversamplers with Scikit-Learn clustering algorithms to address within-class imbalances. Rather than re-assessing the performance of these algorithms, which has already been thoroughly evaluated in prior studies, this paper focuses on their software design, implementation, and practical use within a unified framework.
Descrição
Douzas, G., & Bação, F. (2026). imbalanced-learn-extra: A Python Package for Novel Oversampling Algorithms. Journal of Open Research Software, 14(1), Article 1. https://doi.org/10.5334/jors.459 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project – UID/04152/2025 – Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS – https://doi.org/10.54499/UID/04152/2025 (2025-01-01/2028-12-31) and UID/PRR/04152/2025 https://doi.org/10.54499/UID/PRR/04152/2025 (2025-01-01/2026-06-30) and the project ENHANCE – Enhancing Sustainable Travel in Small Cities and Outer Metropolitan Areas (DUT/0004/2022) https://doi.org/10.54499/DUT/0004/2022.
Palavras-chave
Machine Learning Classification Imbalanced Learning Oversampling Clustering Geometric SMOTE Scikit-Learn Software Information Systems Library and Information Sciences