Anomaly Detection Algorithms Performance

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. 

本数据集收录了主流异常检测（Anomaly Detection, AD）算法在9000个异常检测基准数据集上的性能评估结果，具体包含受试者工作特征曲线下面积（Area Under the ROC Curve, AUC）与平均精度（Average Precision, AP）两项核心指标。该数据集最初随以下学术论文发布：Kandanaarachchi, S., Muñoz, M. A., Hyndman, R. J., & Smith-Miles, K. (2020). 无监督离群点检测的归一化与算法选择. 《数据挖掘与知识发现》, 34(2), 309-354. 本次算法性能评估通过Python语言的PyOD工具包完成。