arXiv:2602.09329v3 Announce Type: replace Abstract: 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 introduce MacrOData, a lar
The proliferation of AI applications necessitates more robust and diverse datasets for foundational tasks like outlier detection, leading to an immediate need for improved benchmarks.
Improved benchmarks for outlier detection are critical for advancing AI reliability and security, particularly in sensitive applications where anomalies signify critical events.
The introduction of MacrOData provides a significantly larger and more diverse benchmark for tabular outlier detection, enabling more accurate assessment and development of OD algorithms.
- · AI researchers
- · Data scientists
- · Industries relying on anomaly detection
- · Developers relying on outdated benchmarks
More accurate and reliable outlier detection models will be developed.
Enhanced anomaly detection will improve fraud detection, cybersecurity, and predictive maintenance across various sectors.
The increased confidence in AI systems for critical functions could accelerate AI adoption in highly regulated industries.
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Read at arXiv cs.LG