Generating Artificial Outliers in the Absence of Genuine Ones -- a Survey

TL;DR

This survey reviews and compares various methods for generating artificial outliers, highlighting their differences, experimental performance, and potential for guiding future research in outlier detection.

Contribution

It systematically categorizes and analyzes existing approaches for artificial outlier generation, providing a comprehensive framework and decision process for selecting suitable methods.

Findings

Generation quality varies widely across approaches

Existing methods cover diverse concepts but have room for improvement

Experimental results highlight differences in effectiveness depending on data sets

Abstract

By definition, outliers are rarely observed in reality, making them difficult to detect or analyse. Artificial outliers approximate such genuine outliers and can, for instance, help with the detection of genuine outliers or with benchmarking outlier-detection algorithms. The literature features different approaches to generate artificial outliers. However, systematic comparison of these approaches remains absent. This surveys and compares these approaches. We start by clarifying the terminology in the field, which varies from publication to publication, and we propose a general problem formulation. Our description of the connection of generating outliers to other research fields like experimental design or generative models frames the field of artificial outliers. Along with offering a concise description, we group the approaches by their general concepts and how they make use of genuine instances. An extensive experimental study reveals the differences between the generation approaches when ultimately being used for outlier detection. This survey shows that the existing approaches already cover a wide range of concepts underlying the generation, but also that the field still has potential for further development. Our experimental study does confirm the expectation that the quality of the generation approaches varies widely, for example, in terms of the data set they are used on. Ultimately, to guide the choice of the generation approach in a specific context, we propose an appropriate general-decision process. In summary, this survey comprises, describes, and connects all relevant work regarding the generation of artificial outliers and may serve as a basis to guide further research in the field.