Comparison of Machine Learning and Statistical Approaches of Detecting Anomalies Using a Simulation Study - Klaudia Lenart (Ekonometria)
Aim: An anomaly is an observation or a group of observations that is unusual for a given dataset. Anomaly detection has many applications, not only as a step of data preparation but also, for example, as a way of identifying credit card fraud detection, network intrusions and much more. There are diverse methods of anomaly detection. In particular two groups of methods have been developed independently – statistical methods and machine learning algorithms. Those methods are rarely compared. While statistical methods focus on formulating a measure of the abnormality of the observations, supervised machine learning makes it possible to use data about typical observations and previously identified anomalies. The aim of this paper was to compare the two approaches by conducting a simulation study.