The Odd One Out: Identifying and Characterising Anomalies

In many situations there exists an abundance of positive examples, but only a handful of negatives. In this paper we show how in binary or transaction data such rare cases can be identified and characterised. Our approach uses the Minimum Description Length principle to decide whether an instance is drawn from the training distribution or not. By using frequent itemsets to construct this compressor, we can easily and thoroughly characterise the decisions, and explain what changes in an example would lead to a different verdict. Furthermore, we give a technique through which, given only a few negative examples, the decision landscape and optimal boundary can be predicted—making the approach parameter-free. Experimentation on benchmark and real data shows our method provides very high classification accuracy, thorough and insightful characterisation of decisions, predicts the decision landscape reliably, and can pinpoint observation errors. Moreover, a case study on real MCADD data shows we provide an interpretable approach with state-of-the-art performance for screening newborn babies for rare diseases.