构建首个大规模表格数据不平衡学习基准，揭示方法选择需依数据特性而定。
TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes
- 系统评测40+算法在57个数据集上的表现
- 20万次实验显示无通用最优方法
- 为实际应用提供基于数据特征的选择建议
不平衡学习仍是表格数据应用中的核心挑战。尽管数十年研究提出了众多算法，但对不同方法在多样数据特征下的表现仍缺乏系统性实证理解，尤其不清楚各类方法在预测性能、鲁棒性及计算可扩展性方面的差异。本文提出表格不平衡学习基准（TILBench），一个大规模的实证基准，评估超过40种代表性算法在57个多样化表格数据集上的表现，涵盖超过20万次受控实验，覆盖广泛的数据特征。结果表明，没有任何一种方法在所有场景中始终占优；相反，不平衡学习方法的有效性强烈依赖于数据特征和计算约束。基于此，我们为真实应用场景提供了实用的方法选择建议。
原文摘要 · Abstract (English)
Imbalanced learning remains a fundamental challenge in tabular data applications. Despite decades of research and numerous proposed algorithms, a systematic empirical understanding of how different imbalanced learning methods behave across diverse data characteristics is still lacking. In particular, it remains unclear how different method families compare in predictive performance, robustness under varying data characteristics, and computational scalability. In this work, we present Tabular Imbalanced Learning Benchmark (TILBench), a large-scale empirical benchmark for tabular imbalanced learning. TILBench evaluates more than 40 representative algorithms across 57 diverse tabular datasets, resulting in over 200000 controlled experiments across a wide range of data characteristics. Our findings show that no single method consistently dominates across all settings; instead, the effectiveness of imbalanced learning methods depends strongly on dataset characteristics and computational constraints. Based on these findings, we provide practical recommendations for selecting appropriate methods in real-world applications.
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