Affiliations: [a] University of Cape Town, Rondebosch, Cape Town, South Africa | [b] African Institute of Mathematical Sciences, Muizenburg, Cape Town, South Africa | [c] South African Radio Astronomical Observatory, Observatory, Cape Town, South Africa | [d] South African Astronomical Observatory, Observatory, Cape Town, South Africa
Correspondence:
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Corresponding author: Ethan Roberts, University of Cape Town, Rondebosch, Cape Town, 7700, South Africa. E-mail: [email protected].
Abstract: Statistical uncertainties are rarely incorporated into machine learning algorithms, especially for anomaly detection. Here we present the Bayesian Anomaly Detection And Classification (BADAC) formalism, which provides a unified statistical approach to classification and anomaly detection within a hierarchical Bayesian framework. BADAC deals with uncertainties by marginalising over the unknown, true, value of the data. Using simulated data with Gaussian noise as an example, BADAC is shown to be superior to standard algorithms in both classification and anomaly detection performance in the presence of uncertainties. Additionally, BADAC provides well-calibrated classification probabilities, valuable for use in scientific pipelines. We show that BADAC can work in online mode and is fairly robust to model errors, which can be diagnosed through model-selection methods. In addition it can perform unsupervised new class detection and can naturally be extended to search for anomalous subsets of data. BADAC is therefore ideal where computational cost is not a limiting factor and statistical rigour is important. We discuss approximations to speed up BADAC, such as the use of Gaussian processes, and finally introduce a new metric, the Rank-Weighted Score (RWS), that is particularly suited to evaluating an algorithm’s ability to detect anomalies.
Keywords: Machine learning, anomalies, classification, novelty, Bayesian, unsupervised class detection
