AI RESEARCH

MacrOData: New Benchmarks of Thousands of Datasets for Tabular Outlier Detection

arXiv CS.LG

ArXi:2602.09329v2 Announce Type: replace Quality benchmarks are essential for fairly and accurately tracking scientific progress and enabling practitioners to make informed methodological choices. Outlier detection (OD) on tabular data underpins numerous real-world applications, yet existing OD benchmarks remain limited. The prominent OD benchmark AdBench is the de facto standard in the literature, yet comprises only 57 datasets. In addition to other shortcomings discussed in this work, its small scale severely restricts diversity and statistical power. We