UNSUPERVISED MACHINE LEARNING ENSEMBLE FOR ANOMALY DETECTION
First Claim
1. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to:
- identify a collection of data, wherein the collection of data comprises data generated by a plurality of sensors;
generate a set of feature vectors from the collection of data;
execute a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the set of feature vectors;
generate a set of pseudo labels based on predictions made during execution of the plurality of unsupervised anomaly detection machine learning algorithms using the set of feature vectors; and
execute a supervised machine learning algorithm using the set of pseudo labels as training data to determine an anomaly detection model corresponding to the plurality of sensors.
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Accused Products
Abstract
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.
62 Citations
20 Claims
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1. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to:
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identify a collection of data, wherein the collection of data comprises data generated by a plurality of sensors; generate a set of feature vectors from the collection of data; execute a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the set of feature vectors; generate a set of pseudo labels based on predictions made during execution of the plurality of unsupervised anomaly detection machine learning algorithms using the set of feature vectors; and execute a supervised machine learning algorithm using the set of pseudo labels as training data to determine an anomaly detection model corresponding to the plurality of sensors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14)
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15. A method comprising:
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identifying a collection of data, wherein the collection of data comprises data generated by a plurality of sensors; generating a set of feature vectors from the collection of data; executing a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the set of feature vectors to generate a set of pseudo labels; and executing a supervised machine learning algorithm using the set of pseudo labels as training data, to determine an anomaly detection model corresponding to the plurality of sensors.
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16. A system comprising:
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a data processor device; computer memory; and an anomaly detection model generator, executable by the data processor device to; receive sensor data generated by a plurality of sensors; determine a plurality of feature vectors from the sensor data; execute a plurality of unsupervised anomaly detection machine learning algorithms in an ensemble using the plurality of feature vectors to generate a set of predictions; determine, from the set of predictions, respective entropy-based weightings for each of the plurality of unsupervised anomaly detection machine learning algorithms; generate a set of pseudo labels based on the predictions and weightings, wherein the set of pseudo labels represents a ground truth; and execute a supervised machine learning algorithm using the set of pseudo labels as training data, to generate an anomaly detection model corresponding to the plurality of sensors. - View Dependent Claims (17, 18, 19, 20)
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Specification