SYSTEM AND METHOD FOR ONE-CLASS SIMILARITY MACHINES FOR ANOMALY DETECTION

摘要

One embodiment provides a system for facilitating anomaly detection. During operation, the system determines, by a computing device, a set of training instances, wherein a training instance represents a single class of data within a predefined range. The system computes a similarity score for each testing instance in a set of testing instances, wherein the similarity score is based on a similarity function which takes as input a respective testing instance and the set of training instances. The system determines a boundary threshold based on an ordering of the similarity score for each testing instance. The system classifies a first testing instance as an anomaly responsive to determining that the first testing instance falls outside the boundary threshold, thereby enhancing data mining and outlier detection in the single class of data using unlabeled training instances.