UNSUPERVISED ANOMALY DETECTION VIA SUPERVISED METHODS

摘要

Techniques for implementing unsupervised anomaly detection via supervised methods are provided. In one set of embodiments, a computer system can train an unsupervised anomaly detection classifier using an unlabeled training data set and classify the unlabeled training data set via the trained version of the unsupervised classifier, where the classifying generates anomaly scores for the data instances in the unlabeled training data set. The computer system can further construct a labeled training data set that includes a first subset of data instances from the unlabeled training data set whose anomaly scores are below a first threshold and a second subset of data instances from the unlabeled training data set whose anomaly scores are above a second threshold. The computer system can then train a supervised anomaly detection classifier using the labeled training data set.