TY  - JOUR
AB  - 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.
AU  - Schlieper, P.*
AU  - Luft, H.*
AU  - Klede, K.*
AU  - Strohmeyer, C.*
AU  - Eskofier, B.M.
AU  - Zanca, D.*
C1  - 70961
C2  - 55838
CY  - 1601 Broadway, 10th Floor, New York, Ny Usa
TI  - Enhancing unsupervised outlier model selection: A study on ireos algorithms.
JO  - ACM Trans. Knowl. Discov. Data
VL  - 18
IS  - 7
PB  - Assoc Computing Machinery
PY  - 2024
SN  - 1556-4681
ER  -