imbalanced-ensemble and awesome-imbalanced-learning
The first is a practical implementation toolbox for class-imbalanced ensemble methods, while the second is a curated resource index that documents papers, implementations, and frameworks in the field—making them complementary rather than competitive, as one provides actionable tools while the other provides discovery and reference material.
About imbalanced-ensemble
ZhiningLiu1998/imbalanced-ensemble
🛠️ Class-imbalanced Ensemble Learning Toolbox. | 类别不平衡/长尾机器学习库 [NeurIPS'25]
This tool helps data professionals build more accurate predictive models when their datasets have very skewed categories, like detecting a rare disease or fraudulent transactions. It takes your existing tabular data and applies advanced techniques to balance the categories, outputting a more robust and reliable classification model. Data scientists, machine learning engineers, and analysts working with real-world, messy datasets will find this valuable.
About awesome-imbalanced-learning
ZhiningLiu1998/awesome-imbalanced-learning
😎 Everything about class-imbalanced/long-tail learning: papers, codes, frameworks, and libraries | 有关类别不平衡/长尾学习的一切：论文、代码、框架与库
This project helps data scientists and machine learning engineers address the common 'class imbalance' problem, where some categories in a dataset have significantly fewer examples than others. It provides a curated collection of research papers, code implementations, and software libraries designed to improve the accuracy of classification models built with imbalanced data, ultimately leading to better predictive performance for rare events.
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