Abstract: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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.14915 [cs.LG] |
| (or arXiv:2605.14915v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.14915 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiaqi Luo [view email]
[v1]
Thu, 14 May 2026 14:50:14 UTC (1,411 KB)