Nonlinear Dynamics

, Volume 61, Issue 1–2, pp 303–310 | Cite as

Anomaly detection combining one-class SVMs and particle swarm optimization algorithms

  • Jiang Tian
  • Hong Gu
Original Paper


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, but the false positive rate is always high and parameter selection is a key issue. So, we propose a novel one-class framework for detecting anomalies, which takes the advantages of both boundary movement strategy and the effectiveness of evaluation algorithm on parameters optimization. First, we search the parameters by using a particle swarm optimization algorithm. Each particle suggests a group of parameters, the area under receiver operating characteristic curve is chosen as the fitness of the object function. Second, we improve the original decision function with a boundary movement. After the threshold has been adjusted, the final detection function will bring about a high detection rate with a lower false positive rate. Experimental results on UCI data sets show that the proposed method can achieve better performance than other one class learning schemes.


Outlier detection Particle swarm optimization Support vector machine Anomaly detection One-class classification 


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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringDalian University of TechnologyDalianChina

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