למידה טבלארית שקועה: מבט כללי, תקן והדרך המעשית

תקציר מקורי באנגליתarXiv:2605.14915v2 Announce Type: replace Abstract: Imbalanced learning remains a fundamental challenge in tabular data applications. Despite decades of research and numerous proposed methods, there is still limited systematic understanding of how different imbalance-handling strategies perform across diverse data regimes and computational constraints, making practical method selection difficult. In this work, we provide a systematic survey of tabular imbalanced learning and introduce Tabular Imbalanced Learning Benchmark (TILBench), a large-scale empirical benchmark for evaluating existing methods. We first organize imbalanced learning approaches into a unified taxonomy and then benchmark more than 40 representative methods across 57 tabular datasets under a standardized evaluation protoc