OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox

摘要

This paper introduces the modular anomaly detection toolbox OCADaMi that incorporates machine learning and visual analytics. The case often encountered in practice where no or only a non-representative number of anomalies exist beforehand is addressed, which is solved using one-class classification. Target users are developers, engineers, test engineers and operators of technical systems. The users can interactively analyse data and define workflows for the detection of anomalies and visualisation. There is a variety of application-domains, e.g. manufacturing or testing of automotive systems. The functioning of the system is shown for fault detection in real-world automotive data from road trials. A video is available: https://youtu.be/DylKkpLyfMk.