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Anomaly Detection via Coupled Gaussian Kernels

  • Guichong Li
  • Nathalie Japkowicz
  • Lian Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)

Abstract

Anomaly detection using One-Class Support Vector Machine (OCSVM) have attracted wide attention in practical applications. Recent research focuses on enhancing OCSVM using either ensemble learning techniques or Multiple Kernel Learning (MKL) since single kernels such as the Gaussian Radial-Based Function (GRBF) kernel might not be flexible enough to construct a proper feature space. In this paper, we develop a new kernel, called centralized GRBF. Further, the two GRBF and centralized GRBF are combined by using a new ensemble kernel technique, called Coupled Ensemble-Kernels (CEK), to improve OCSVM for anomaly detection. Therefore, the final classification model is itself a large-margin classifier while it is actually an ensemble classifier coined with two sub-large-margin models. We show that the proposed CEK outperforms previous approaches using traditional ensemble learning methods and MKL for anomaly detection.

Keywords

Anomaly Detection Kernel-Based Methods One-Class Support Vector Machine Multiple Kernel Learning Ensemble Learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Guichong Li
    • 1
  • Nathalie Japkowicz
    • 1
  • Lian Yang
    • 1
  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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