Skip to main content
Article thumbnail
Location of Repository

General support vector representation machine for one-class classification of non-stationary classes

By Fatih Camci and R. B. Chinnam


Novelty detection, also referred to as one-class classification, is the process of detecting 'abnormal' behavior in a system by learning the 'normal' behavior. Novelty detection has been of particular interest to researchers in domains where it is difficult or expensive to find examples of abnormal behavior (such as in medical/equipment diagnosis and IT network surveillance). Effective representation of normal data is of primary interest in pursuing one-class classification. While the literature offers several methods for one-class classification, very few methods can support representation of non-stationary classes without making stringent assumptions about the class distribution. This paper proposes a one-class classification method for non-stationary classes using a modified support vector machine and an efficient online version for reducing computational time. The presented method is applied to several simulated datasets and actual data from a drilling machine. In addition, we present comparison results with other methods that demonstrate its superior performance. (C) 2008 Elsevier Ltd. All rights reserved

Topics: Novelty detection, One-class classification, Support vector machine, Non-stationary classes, Non-stationary processes, Online training, Outlier detection
Publisher: Elsevier Science B.V., Amsterdam.
Year: 2008
DOI identifier: 10.1016/j.patcog.2008.04.001
OAI identifier:
Provided by: Cranfield CERES

Suggested articles


  1. A bootstrap-like rejection mechanism for multilayer perceptron networks”, II Simposio Brasileiro de Redes Neurais, São Carlos-SP,
  2. (2003). A kernel-distance-based multivariate control chart using support vector methods. doi
  3. (2004). An Approach To Novelty Detection Applied To The Classification Of Image Regions, doi
  4. (2001). An introduction to kernel-based learning algorithms”,. doi
  5. (2000). An introduction to support vector machines and other kernel-based learning methods, doi
  6. (2001). Automated segmentation of multiple sclerosis lesions by model outlier detection”, doi
  7. (1995). Automatic assessment of scintmammographic images using a novelty filter”, in
  8. (2005). Change Detection in Time Series Data Using Wavelet Footprints”, doi
  9. (1997). Choosing an appropriate model for novelty detection”, in doi
  10. (1997). Detecting attacks on networks”, doi
  11. (1997). Development and benchmarking of multivariate statistical process control tools for a semiconductor etch process; improving robustness through model updating”, in
  12. (1999). Event Detection from Time Series Data”, in doi
  13. (2003). Experimental validation of structural health monitoring methodology I: novelty detection on a laboratory structure”, doi
  14. (2003). Experimental validation of structural health monitoring methodology”, doi
  15. (2000). Knowledge Discovery and Data Mining - The Info-Fuzzy Network (IFN) doi
  16. (1996). Learning in the Presence of Concept Drift and Hidden Contexts”, doi
  17. (2004). Milne “Online unsupervised outlier detection using finite mixtures with discounting learning algorithms”, doi
  18. (1995). Novelty detection for the identification of masses in mammograms”, in doi
  19. (2003). Novelty detection: a review-part 1: neural network based approaches, doi
  20. (1976). On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density Functions”. doi
  21. (2001). One Class Classification”, doi
  22. (2002). Online Classification of Nonstationary Data Streams”,
  23. (2003). Online novelty detection on temporal sequences”, in doi
  24. (2000). Recursive PCA for adaptive process monitoring”, doi
  25. (1991). Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners”, Pattern Analysis and Machine Intelligence, doi
  26. (1998). Statistical learning theory, doi
  27. (1999). Support vector domain description”, doi
  28. (2003). Support vector machines for class representation and discrimination”, doi
  29. (1999). SV Estimation of a Distribution’s Support”, in
  30. (2006). Unifying Framework for Detecting Outliers and Change Points from Non-Stationary Time Series Data”, doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.