TILBench: A Systematic Benchmark for Tabular Imbalanced Learning Across Data Regimes

ArXi:2605.14915v1 Announce Type: new 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.