SORTAD: Self-Supervised Optimized Random Transformations for Anomaly
  Detection in Tabular Data
Guy Hay Pablo Liberman
We consider a self-supervised approach to anomaly detection in tabular data.
Random transformations are applied to the data, and then each transformation is
identified based on its output. These predicted transformations are used to
identify anomalies. In tabular data this approach faces many challenges that
are related to the uncorrelated nature of the data. These challenges affect the
transformations that should be used, as well as the use of their predictions.
To this end, we propose SORTAD, a novel algorithm that is tailor-made to solve
these challenges. SORTAD optimally chooses random transformations that help the
classification process, and have a scoring function that is more sensitive to
the changes in the transformations classification prediction encountered in
tabular data. SORTAD achieved state-of-the-art results on multiple commonly
used anomaly detection data sets, as well as in the overall results across all
data sets tested.