Outlier detection with one-class classifiers from ML and KDD

J.H.M. Janssens; I. Flesch; E.O. Postma

Outlier detection with one-class classifiers from ML and KDD

J.H.M. Janssens, I. Flesch, E.O. Postma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.

Original language	English
Title of host publication	Proceedings of the Eight International Conference on Machine Learning and Applications
Editors	A. Wani, M. Kantardzic, V. Palade, L. Kurgan, Y. Qi
Place of Publication	Miami, FL, USA
Publisher	ICMLA
Pages	147-153
Publication status	Published - 2009

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Cite this

@inproceedings{9da05e1f1f9b4cab96d58e5ea952d918,

title = "Outlier detection with one-class classifiers from ML and KDD",

abstract = "Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.",

author = "J.H.M. Janssens and I. Flesch and E.O. Postma",

year = "2009",

language = "English",

pages = "147--153",

editor = "A. Wani and M. Kantardzic and V. Palade and L. Kurgan and Y. Qi",

booktitle = "Proceedings of the Eight International Conference on Machine Learning and Applications",

publisher = "ICMLA",

}

Outlier detection with one-class classifiers from ML and KDD. / Janssens, J.H.M.; Flesch, I.; Postma, E.O.
Proceedings of the Eight International Conference on Machine Learning and Applications. ed. / A. Wani; M. Kantardzic; V. Palade; L. Kurgan; Y. Qi. Miami, FL, USA: ICMLA, 2009. p. 147-153.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Outlier detection with one-class classifiers from ML and KDD

AU - Janssens, J.H.M.

AU - Flesch, I.

AU - Postma, E.O.

PY - 2009

Y1 - 2009

N2 - Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.

AB - Experimental results show that LOF and SVDD are the two best-performing methods. It is concluded that both fields offer outlier-detection methods that are competitive in performance and that bridging the gap between both fields may facilitate the development of outlier-detection methods.

M3 - Conference contribution

SP - 147

EP - 153

BT - Proceedings of the Eight International Conference on Machine Learning and Applications

A2 - Wani, A.

A2 - Kantardzic, M.

A2 - Palade, V.

A2 - Kurgan, L.

A2 - Qi, Y.

PB - ICMLA

CY - Miami, FL, USA

ER -

Outlier detection with one-class classifiers from ML and KDD

Abstract

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