Anomaly Detection Algorithms Performance

Citation Author(s):
Malgorzata
Gutowska
Submitted by:
Malgorzata Gutowska
Last updated:
Tue, 01/17/2023 - 11:54
DOI:
10.21227/27ws-yf50
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Abstract 

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.

 

Instructions: 

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.