Timezone: »

PIDForest: Anomaly Detection via Partial Identification
Parikshit Gopalan · Vatsal Sharan · Udi Wieder

Wed Dec 11 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #60

We consider the problem of detecting anomalies in a large dataset. We propose a framework called Partial Identification which captures the intuition that anomalies are easy to distinguish from the overwhelming majority of points by relatively few attribute values. Formalizing this intuition, we propose a geometric anomaly measure for a point that we call PIDScore, which measures the minimum density of data points over all subcubes containing the point. We present PIDForest: a random forest based algorithm that finds anomalies based on this definition. We show that it performs favorably in comparison to several popular anomaly detection methods, across a broad range of benchmarks. PIDForest also provides a succinct explanation for why a point is labelled anomalous, by providing a set of features and ranges for them which are relatively uncommon in the dataset.

Author Information

Parikshit Gopalan (VMware Research)
Vatsal Sharan (Stanford University)
Udi Wieder (VMware Research)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors