אירועים

Anomaly detection is a technique for finding unusual patterns in the given data. The study of anomaly detection has a long history and spans multiple disciplines including engineering, machine learning, statistics and real-life applications. We consider the problem of anomaly detection in tabular data, and present a detection scheme which is based on training a multiway classification model for discriminating between dozens of transformations applied to given "normal" records. The auxiliary expertise learned by the model generates feature representations that allow, at test time, identification of anomalous records based on the placement of representations of transformed test records with respect to the learned representations. We apply the scheme while incorporating the recently proposed classification models designed for tabular data and present the results obtained for the real-life data.