Back to results
US 2016/0253598 A1Published application
Large-scale anomaly detection with relative density-ratio estimation
AssigneeYahoo! Inc.
InventorsMakoto Yamada, Chao Qin, Hua Ouyang, Achint Thomas, Yi Chang
Life of this patent
Estimating the term…
Abstract
In one embodiment, a set of training data consisting of inliers may be obtained. A supervised classification model may be trained using the set of training data to identify outliers. The supervised classification model may be applied to generate an anomaly score for a data point. It may be determined whether the data point is an outlier based, at least in part, upon the anomaly score.
Claims20
A method, comprising:
- obtaining a set of training data consisting of inliers;
- training a supervised classification model using the set of training data to identify outliers;
- applying the supervised classification model to generate an anomaly score for a data point;
- and
- determining whether the data point is an outlier based, at least in part, upon the anomaly score.
2 more elements
An apparatus, comprising:
- a processor;
- and
- a memory storing thereon computer-readable instructions, the computer-readable instructions being configured to:
- obtain a set of training data consisting of inliers;
- train a supervised classification model using the set of training data to identify outliers;
- apply the supervised classification model to generate an anomaly score for a data point;
- and
- determine whether the data point is an outlier based, at least in part, upon the anomaly score.
5 more elements
A non-transitory computer-readable storage medium, comprising:
- instructions for obtaining a set of training data consisting of inliers;
- instructions for training a supervised classification model using the set of training data to identify outliers;
- instructions for applying the supervised classification model to generate an anomaly score for a data point;
- and
- instructions for determining whether the data point is an outlier based, at least in part, upon the anomaly score.
2 more elements
Dependencies are inferred from the claim text.