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UNSUPERVISED MACHINE LEARNING ENSEMBLE FOR ANOMALY DETECTION
UNSUPERVISED MACHINE LEARNING ENSEMBLE FOR ANOMALY DETECTION
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机译:机器学习异常,可进行异常检测
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摘要
An anomaly detection model generator accesses sensor data generated by a plurality of sensors, determines a plurality of feature vectors from the sensor data, and executes a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the plurality of feature vectors to generate a set of predictions. Respective entropy-based weightings are determined for each of the plurality of unsupervised anomaly detection machine learning algorithms from the set of predictions. A set of pseudo labels is generated based on the predictions and weightings, and a supervised machine learning algorithm uses the set of pseudo labels as training data to generate an anomaly detection model corresponding to the plurality of sensors.
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