Adaptively generating outlier scores using histograms
AssigneeInternational Business Machines Corporation
InventorsYair ALLOUCHE, Aviad COHEN, Samuel Solomon ACKERMAN, Ofer Haim BILLER, Eitan Daniel FARCHI
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Abstract
An example system includes a processor to receive a stream of records. The processor can generate an unbiased outlier score for each sample in the stream of records via a trained histogram-based outlier score model. The unbiased outlier score is unbiased for samples including dependent features using feature grouping. The processor can then detect an anomaly in response to detecting that an associated unbiased outlier score of the sample is higher than a predefined threshold.
Claims20
A system, comprising a processor to:
- receive a stream of records;
- generate an unbiased outlier score for each sample in the stream of records via a trained histogram-based outlier score model, wherein the unbiased outlier score is unbiased for samples including dependent features using feature grouping;
- and
- detect an anomaly in response to detecting that an associated unbiased outlier score of the sample is higher than a predefined threshold.
A computer-implemented method, comprising:
- receiving, via a processor, a stream of records;
- inputting, via the processor, samples from the stream of records into a trained histogram-based outlier score model to generate an unbiased outlier score for the samples, wherein the unbiased outlier score is unbiased for samples including dependent features using feature grouping;
- and
- detecting, via the processor, an anomaly in response to detecting that an unbiased of a sample is higher than a predefined threshold.
A computer program product for detecting anomalies in data streams, the computer program product comprising a computer-readable storage medium having program code embodied therewith, the program code executable by a processor to cause the processor to:
- receive a stream of records;
- generate an unbiased outlier score for each sample in the stream of records via a trained histogram-based outlier score model, wherein the unbiased outlier score is unbiased for samples including dependent features using feature grouping;
- and
- detect an anomaly in response to detecting that an associated unbiased outlier score of the sample is higher than a predefined threshold.
Dependencies are inferred from the claim text.