Anomaly Detection Algorithms Performance
- Citation Author(s):
- Submitted by:
- Malgorzata Gutowska
- Last updated:
- Tue, 01/17/2023 - 11:54
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The dataset contains performance values, Area Under the ROC Curve (AUC) and Average Precision (AP), of popular anomaly detection (AD) algorithms taken over a set of 9k AD benchmark datasets.
Datasets were initially published with the following paper:
Kandanaarachchi, S., Muñoz, M. A., Hyndman, R. J., & Smith-Miles, K. (2020). On normalization and algorithm selection for unsupervised outlier detection. Data Mining and Knowledge Discovery, 34(2), 309-354.
Python PyOD package has been used to perform the evaluation of the algorithms.
The *.csv files contain AUC and AP performance metrics of anomaly detection (AD) algorithms. Row names contain AD benchmark dataset names and column names indicate the AD algorithm and associated parameters.
- anomaly_detection_performance_data.zip (12.08 MB)