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Schlieper, P.* ; Luft, H.* ; Klede, K.* ; Strohmeyer, C.* ; Eskofier, B.M. ; Zanca, D.*

Enhancing unsupervised outlier model selection: A study on ireos algorithms.

ACM Trans. Knowl. Discov. Data 18:25 (2024)
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Outlier detection stands as a critical cornerstone in the field of data mining, with a wide range of applications spanning from fraud detection to network security. However, real-world scenarios often lack labeled data for training, necessitating unsupervised outlier detection methods. This study centers on Unsupervised Outlier Model Selection (UOMS), with a specific focus on the family of Internal, Relative Evaluation of Outlier Solutions (IREOS) algorithms. IREOS measures outlier candidate separability by evaluating multiple maximum-margin classifiers and, while effective, it is constrained by its high computational demands. We investigate the impact of several different separation methods in UOMS in terms of ranking quality and runtime. Surprisingly, our findings indicate that different separability measures have minimal impact on IREOS' effectiveness. However, using linear separation methods within IREOS significantly reduces its computation time. These insights hold significance for real-world applications where efficient outlier detection is critical. In the context of this work, we provide the code for the IREOS algorithm and our separability techniques.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Anomaly Detection ; Model Selection ; Outlier Detection ; Unsupervised Evaluation
ISSN (print) / ISBN 1556-4681
e-ISSN 1556-472X
Journal ACM Transactions on Knowledge Discovery from Data
Quellenangaben Volume: 18, Issue: 7, Pages: , Article Number: 25 Supplement: ,
Publisher Association for Computing Machinery
Publishing Place 1601 Broadway, 10th Floor, New York, Ny Usa
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Schaeffler Hub for Advanced Research at the Friedrich-Alexander-Universitat Erlangen-Nurnberg