Have better understanding of anomaly detection algorithms: please read our paper for details.We envision three primary usages of ADBench: The Figure below provides an overview of our proposed ADBench (see our paper for details). ⁉️ and many more can be found in our papers (Section 4).
semi-supervised methods show potential in achieving robustness in noisy and corrupted data, possibly due to their efficiency in using labels and feature selection.in controlled environments, we observe that best unsupervised methods for specific types of anomalies are even better than semi- and fully-supervised methods, revealing the necessity of understanding data characteristics.‼️ with merely 1% labeled anomalies, most semi-supervised methods can outperform the best unsupervised method, justifying the importance of supervision.‼️ surprisingly none of the benchmarked unsupervised algorithms is statistically better than others, emphasizing the importance of algorithm selection.Key Takeaways: Adbench answers many questions for both researchers with interesting findings: algorithm robustness and stability under 3 settings of data corruptions.Simulating the environments with 4 types of anomalies and algorithm performance under different types of anomalies by.
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