构建首个表格数据类别不平衡学习综合基准，支持算法对比与实践指导。
CLIMB: Class-imbalanced Learning Benchmark on Tabular Data
- 整合73个真实世界数据集与29种主流算法，统一接口便于比较
- 实验证明简单重平衡效果有限，集成方法更优，数据质量关键
- 适合机器学习研究者与工业界从业者优化不平衡分类任务
表格数据中的类别不平衡学习在诸多实际应用中至关重要，因少数类代表关键但稀有的结果。本文提出CLIMB，一个面向表格数据类别不平衡学习的综合性基准。CLIMB包含跨多个领域和不平衡程度的73个真实世界数据集，以及29种代表性不平衡学习算法的统一实现。基于高质量开源Python包，具备统一API设计、详尽文档与严格代码质量控制，支持算法的便捷实现与对比。通过大量实验，我们提供了关于方法准确率与效率的实用洞见，揭示了简单重平衡的局限性、集成方法的有效性，以及数据质量的重要性。代码、文档与示例已公开于https://github.com/ZhiningLiu1998/imbalanced-ensemble。
原文摘要 · Abstract (English)
Class-imbalanced learning (CIL) on tabular data is important in many real-world applications where the minority class holds the critical but rare outcomes. In this paper, we present CLIMB, a comprehensive benchmark for class-imbalanced learning on tabular data. CLIMB includes 73 real-world datasets across diverse domains and imbalance levels, along with unified implementations of 29 representative CIL algorithms. Built on a high-quality open-source Python package with unified API designs, detailed documentation, and rigorous code quality controls, CLIMB supports easy implementation and comparison between different CIL algorithms. Through extensive experiments, we provide practical insights on method accuracy and efficiency, highlighting the limitations of naive rebalancing, the effectiveness of ensembles, and the importance of data quality. Our code, documentation, and examples are available at https://github.com/ZhiningLiu1998/imbalanced-ensemble.
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