Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):
A. Hartl, F. Iglesias Vazquez, T. Zseby:
"SDOstream: Low-Density Models for Streaming Outlier Detection";
Poster: 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning,
Bruges, Belgium;
02.10.2020
- 04.10.2020; in: "ESANN 2020 - Proceedings",
i6doc.com,
Belgium
(2020),
ISBN: 978-2-87587-074-2;
S. 661
- 666.
Kurzfassung englisch:
Data commonly changes over time. Algorithms for anomaly detection must therefore be adapted to overcome the challenges of evolving data. We present SDOstream, a distance-based outlier detection algorithm for stream data that uses low-density models, therefore operating in linear time and avoiding the limitations of sliding windows and instance-based methods. SDOstream is designed to ensure a good integration in applications, hence the definition of "outlier" is not predetermined, but can be decided by the application based on distances to representative point locations. We describe the algorithm and evaluate algorithm performance with several datasets.
Schlagworte:
Outlier detection, stream data, low-density models
Elektronische Version der Publikation:
https://www.esann.org/sites/default/files/proceedings/2020/ES2020-143.pdf
Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.