Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores

Lüer, Fiete; Weber, Tobias ; Dolgich, Maxim und Böhm, Christian (2023): Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores. 23rd IEEE International Conference on Data Mining (IEEE ICDM), Shanghai, China, 01.-04. Dezember 2023. Wang, Jihe; He, Yi; Dinh, Thang N.; Grant, Christan; Qiu, Meikang und Pedrycz, Witold (Hrsg.): In: 23rd IEEE International Conference on Data Mining Workshops, Piscataway, NJ: IEEE. S. 550-559

Abstract

Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a β-variational autoencoder (VAE) with the discriminative strengths of generative adversarial networks (GANs), we propose a novel model, β-VAEGAN. We investigate methods for composing anomaly scores based on the discriminative and reconstructive capabilities of our model. Existing work focuses on linear combinations of these components to determine if data is anomalous. We advance existing work by training a kernelized support vector machine (SVM) on the respective error components to also consider nonlinear relationships. This improves anomaly detection performance, while allowing faster optimization. Lastly, we use the deviations from the Gaussian prior of β-VAEGAN to form a novel anomaly score component. In comparison to state-of-the-art work, we improve the F 1 score during anomaly detection from 0.85 to 0.92 on the widely used MITBIH Arrhythmia Database.

• Dokumententyp: Konferenzbeitrag (Paper)
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Lehrstühle/Arbeitsgruppen > Computationale Statistik ; Mathematik, Informatik und Statistik > Informatik
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik ; 500 Naturwissenschaften und Mathematik > 510 Mathematik ; 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISBN: 979-8-3503-8164-1 ; 979-8-3503-8165-8
• Ort: Piscataway, NJ
• Sprache: Englisch