OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox
Abstract
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 .
Cite
Text
Theissler et al. "OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019. doi:10.1007/978-3-030-46133-1_47Markdown
[Theissler et al. "OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2019.](https://mlanthology.org/ecmlpkdd/2019/theissler2019ecmlpkdd-ocadami/) doi:10.1007/978-3-030-46133-1_47BibTeX
@inproceedings{theissler2019ecmlpkdd-ocadami,
title = {{OCADaMi: One-Class Anomaly Detection and Data Mining Toolbox}},
author = {Theissler, Andreas and Frey, Stephan and Ehlert, Jens},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2019},
pages = {764-768},
doi = {10.1007/978-3-030-46133-1_47},
url = {https://mlanthology.org/ecmlpkdd/2019/theissler2019ecmlpkdd-ocadami/}
}