Incorporating User Feedback Into One-Class Support Vector Machines for Anomaly Detection

Julien Lesouple (1) , Jean-Yves Tourneret (1, 2, 3)

1 TéSA - Télécommunications Spatiales et Aéronautiques - Telecommunications for Space ant Aeronautics
2 IRIT-MINDS - CoMputational imagINg anD viSion
3 Toulouse INP - Institut National Polytechnique (Toulouse)

Machine learning and data-driven algorithms have gained a growth of interest during the past decades due to the computation capability of the computers which has increased and the quantity of data available in various domains. One possible application of machine learning is to perform unsupervised anomaly detection. Indeed, among all available data, the anomalies are supposed to be very sparse and the expert might not have the time to label all the data as nominal or not. Many solutions exist to this unsupervised problem, but are known to provide many false alarms, because some scarce nominal modes might not be included in the training dataset and thus will be detected as anomalies. To tackle this issue, we propose to present an existing iterative algorithm, which presents potential anomaly to the expert at each iteration, and compute a new boundary according to this feedback using One Class Support Vector Machine.

Mots clés: Machine learning; Semi-supervised Learning; Active Learning; User Feedback; Anomaly Detection; One-Class Support Vector Machines

Domaines: Traitement du signal et de l'image [eess.SP]; Machine Learning [stat.ML]

Julien Lesouple, Jean-Yves Tourneret. Incorporating User Feedback Into One-Class Support Vector Machines for Anomaly Detection. 28th European Signal Processing Conference (EUSIPCO 2020), Jan 2021, Amsterdam, France. pp.1608-1612, ⟨10.23919/Eusipco47968.2020.9287325⟩. ⟨hal-03693899⟩