Anomaly detection
OwnerLorne Schell, Fanny C. Riols +2inventors
InventorsLorne Schell, Fanny C. Riols, Pegah Kamousi, Elena Busila
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Abstract
A training dataset for anomaly detection is received. An unsupervised machine learning model is trained using at least a portion of the training dataset to generate a trained unsupervised machine learning model. A supervised machine learning model is trained using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model to generate a trained supervised machine learning model. Both the trained unsupervised machine learning model and the trained supervised machine learning model are provided for combined use in machine learning anomaly detection inference.
Claims20
A method comprising:
- receiving a training dataset for anomaly detection;
- training an unsupervised machine learning model using at least a portion of the training dataset, to generate a trained unsupervised machine learning model;
- training a supervised machine learning model using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model, to generate a trained supervised machine learning model;
- and
- providing both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference.
A system comprising:
- one or more processors;
- and
- a memory coupled to the one or more processors, wherein the memory is configured to provide the one or more processors with instructions which when executed cause the one or more processors to:
- receive a training dataset for anomaly detection;
- train an unsupervised machine learning model using at least a portion of the training dataset, to generate a trained unsupervised machine learning model;
- train a supervised machine learning model using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model, to generate a trained supervised machine learning model;
- and
- provide both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference.
A computer program product, the computer program product being embodied in a non-transitory computer readable storage medium and comprising computer instructions for:
- receiving a training dataset for anomaly detection;
- training an unsupervised machine learning model using at least a portion of the training dataset, to generate a trained unsupervised machine learning model;
- training a supervised machine learning model using an output from the unsupervised machine learning model and an anomaly detection feedback associated with the output from the unsupervised machine learning model, to generate a trained supervised machine learning model;
- and
- providing both the trained unsupervised machine learning model and the trained supervised machine learning model for combined use in machine learning anomaly detection inference.
Dependencies are inferred from the claim text.