Abstract
Anomalies are patterns in data that do not conform to a well-defined notion of normal behavior. One-class Support Vector Machines calculate a hyperplane in the feature space to distinguish anomalies, however, it may not identify the ideal hyperplane especially when the support vectors do not have the overall characteristics of the target data. So, we propose a new local density OCSVM by incorporating distance measurements based on local density degree to reflect the distribution of a given data set. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)
Filzmoser, P., Maronna, R., Werner, M.: Outlier identification in high dimensions. Comput. Stat. Data Anal. 52(3), 1694–1711 (2008)
Scholkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. Adv. Neural Inf. Process. Syst. 12(3), 582–588 (2000)
Scholkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443–1471 (2001)
Tax, D.M.J., Duin, R.P.W.: Support vector domain description. Pattern Recognit. Lett. 20(11–13), 1191–1199 (1999)
Tax, D.M.J., Duin, R.P.W.: Support vector data description. Mach. Learn. 54(1), 45–66 (2004)
Tian, J., Gu, H.: Anomaly detection combining one-class SVMs and particle swarm optimization algorithms. Nonlinear Dyn. 61(1), 303–310 (2010)
King, S.P., King, D.M., Astley, K., Tarassenko, L., Hayton, P., Utete, S.: The use of novelty detection techniques for monitoring high-integrity plant. In: Proceedings of the 2002 International Conference on Control Applications, vol. 1, Anchorage, AK, pp. 221–226 (2002)
Gardner, A.B., Krieger, A.M., Vachtsevanos, G., Litt, B.: One-class novelty detection for seizure analysis from intracranial EEG. J. Mach. Learn. Res. 7(7), 1025–1044 (2006)
Lee, K., Kim, D.W., Lee, K.H., Lee, D.: Density-induced support vector data description. IEEE Trans. Neural Netw. 18(1), 284–289 (2007)
Giacinto, G., Perdisci, R., Del Rio, M., Roli, F.: Intrusion detection in computer networks by a modular ensemble of one-class classifiers. Inf. Fusion 9(1), 69–82 (2008)
Chang, C.C., Lin, C.J. Libsvm: a library for support vector machines, software available at http://www.csie.ntu.edu.tw/cjlin/libsvm [cp] (2001)
Fawcett, T.: An introduction to ROC analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tian, J., Gu, H., Gao, C. et al. Local density one-class support vector machines for anomaly detection. Nonlinear Dyn 64, 127–130 (2011). https://doi.org/10.1007/s11071-010-9851-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11071-010-9851-y