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Cited by 427

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Enhancing one-class support vector machines for unsupervised anomaly detection

Amer, Mennatallah, Goldstein, Markus, Abdennadher, Slim·2013·Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
DOI 10.1145/2500853.2500857
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Abstract

Support Vector Machines (SVMs) have been one of the most successful machine learning techniques for the past decade. For anomaly detection, also a semi-supervised variant, the one-class SVM, exists. Here, only normal data is required for training before anomalies can be detected. In theory, the one-class SVM could also be used in an unsupervised anomaly detection setup, where no prior training is conducted. Unfortunately, it turns out that a one-class SVM is sensitive to outliers in the data. In this work, we apply two modifications in order to make one-class SVMs more suitable for unsupervised anomaly detection: Robust one-class SVMs and eta one-class SVMs. The key idea of both modifications is, that outliers should contribute less to the decision boundary as normal instances. Experiments performed on datasets from UCI machine learning repository show that our modifications are very promising: Comparing with other standard unsupervised anomaly detection algorithms, the enhanced one-class SVMs are superior on two out of four datasets. In particular, the proposed eta one-class SVM has shown the most promising results.

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Metadata

Authors
Amer, M., Goldstein, M., & Abdennadher, S.
Venue
Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description
Publisher
ACM
Published
2013-08-11
Pages
8-15
DOI
10.1145/2500853.2500857
OpenAlex
W2134490011
Cited by
427
References
5
FWCI
10.19
Type
conference paper
Language
en
Open access
closed
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
Stiftung Rheinland-Pfalz für Innovation (961-38 6261/1039)

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Amer, M., Goldstein, M., & Abdennadher, S. (2013). Enhancing one-class support vector machines for unsupervised anomaly detection. Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. https://doi.org/10.1145/2500853.2500857

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