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Online Anomaly Detection Using Pairwise Agreement in Heterogeneous Model Ensemble
Online Anomaly Detection Using Pairwise Agreement in Heterogeneous Model Ensemble
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机译:异构模型集成中使用成对协议在线异常检测
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摘要
Techniques are provided for online anomaly detection using pairwise agreement in a heterogeneous model ensemble. An exemplary contextual model agreement network comprises nodes and transition edges between the nodes, where each node corresponds to a machine learning model and the transition edges between corresponding pairwise machine learning models encode a level of historical agreement between the pairwise machine learning models. In response to an availability of new data observations: features present in the data observations are extracted; a subset of the machine learning models is selected from the machine learning models based on the extracted features; the historical agreement between the selected machine learning models is compared with a current agreement of the selected machine learning models; and an anomaly is detected in the data observations based on the comparison. The contextual model agreement network is optionally updated based on new data observations.
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