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How to introduce expert feedback in one-class support vector machines for anomaly detection?

Lesouple, Julien and Baudoin, Cédric and Spigai, Marc and Tourneret, Jean-Yves How to introduce expert feedback in one-class support vector machines for anomaly detection? (2021) Signal Processing, 188. 108197. ISSN 0165-1684

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Official URL: https://doi.org/10.1016/j.sigpro.2021.108197

Abstract

Anomaly detection consists of detecting elements of a database that are different from the majority of normal data. The majority of anomaly detection algorithms considers unlabeled datasets. However, in some applications, labels associated with a subset of the database (coming for instance from expert feed- back) are available providing useful information to design the anomaly detector. This paper studies a semi-supervised anomaly detector based on support vector machines, which takes the best of existing supervised and unsupervised support vector machines algorithms. The proposed algorithm allows the maximum proportion of vectors detected as anomalies and the maximum proportion of errors in the supervised data to be controlled, through two hyperparameters defining these proportions. Simulations conducted on various benchmark datasets show the interest of the proposed semi-supervised anomaly detection method.

Item Type:Article
HAL Id:hal-03382585
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution:French research institutions > Centre National de la Recherche Scientifique - CNRS (FRANCE)
Université de Toulouse > Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE)
Other partners > Thales (FRANCE)
Université de Toulouse > Université Toulouse III - Paul Sabatier - UT3 (FRANCE)
Université de Toulouse > Université Toulouse - Jean Jaurès - UT2J (FRANCE)
Université de Toulouse > Université Toulouse 1 Capitole - UT1 (FRANCE)
Other partners > Laboratoire de recherche en télécommunications spatiales et aéronautiques - TéSA (FRANCE)
Laboratory name:
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Deposited On:01 Jul 2021 07:17

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