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How to introduce expert feedback in one-class support vector machines for anomaly detection?
- Source :
- Signal Processing, Signal Processing, Elsevier, 2021, 188, pp.108197. ⟨10.1016/j.sigpro.2021.108197⟩
- Publication Year :
- 2021
- Publisher :
- Elsevier, 2021.
-
Abstract
- International audience; 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.
- Subjects :
- Computer science
02 engineering and technology
Semi-supervised learning
Anomaly detection
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Machine learning
0202 electrical engineering, electronic engineering, information engineering
Traitement du signal et de l'image
Electrical and Electronic Engineering
Hyperparameter
Support vector machines
business.industry
Detector
020206 networking & telecommunications
Pattern recognition
Class (biology)
Support vector machine
ComputingMethodologies_PATTERNRECOGNITION
Control and Systems Engineering
Signal Processing
Benchmark (computing)
020201 artificial intelligence & image processing
Computer Vision and Pattern Recognition
Artificial intelligence
Anomaly (physics)
business
Software
Subjects
Details
- Language :
- English
- ISSN :
- 01651684 and 18727557
- Database :
- OpenAIRE
- Journal :
- Signal Processing, Signal Processing, Elsevier, 2021, 188, pp.108197. ⟨10.1016/j.sigpro.2021.108197⟩
- Accession number :
- edsair.doi.dedup.....cd2e56f14ca6c62a433da6ca3bce1a17