Enhancing unsupervised outlier model selection: A study on ireos algorithms

Schlieper P, Luft H, Klede K, Strohmeyer C, Eskofier B, Zanca D (2024)

Publication Language: English

Publication Type: Journal article

Publication year: 2024

Journal

ACM Transactions on Knowledge Discovery from Data Special Interest Group on Computer Graphics, Association for Computing Machinery

Book Volume: 18

Article Number: 178

Journal Issue: 7

DOI: 10.1145/3653719

Abstract

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.

Authors with CRIS profile

Philipp Schlieper Machine Learning and Data Analytics Lab Kai Klede Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab Dario Zanca Machine Learning and Data Analytics Lab

Additional Organisation(s)

Institute Materials for Electronics and Energy Technology (i-MEET) (i-MEET)

Involved external institutions

Schaeffler Technologies AG & Co. KG DE Germany (DE)

How to cite

APA:

Schlieper, P., Luft, H., Klede, K., Strohmeyer, C., Eskofier, B., & Zanca, D. (2024). Enhancing unsupervised outlier model selection: A study on ireos algorithms. ACM Transactions on Knowledge Discovery from Data, 18(7). https://doi.org/10.1145/3653719

MLA:

Schlieper, Philipp, et al. "Enhancing unsupervised outlier model selection: A study on ireos algorithms." ACM Transactions on Knowledge Discovery from Data 18.7 (2024).

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