Selected Random Subspace Novelty Detection Filter

  • Fatma Hamdi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8226)


In this paper we propose a solution to deal with the problem of novelty detection. Given a set of training examples believed to come from the same class, the aim is to learn a model that will be able to distinguich examples in the future that do not belong to the same class. The proposed approach called Selected Random Subspace Novelty Detection Filter (SRS − NDF) is based on the bootstrap technique, the ensemble idea and model selection principle. The SRS − NDF method is compared to novelty detection methods on publicly available datasets. The results show that for most datasets, this approach significantly improves performance over current techniques used for novelty detection.


Ensemble Member Ensemble Method Multi Layer Perceptron Novelty Detection True Negative Rate 
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  1. 1.
    Markou, M., Singh, S.: Novelty detection: a review - part 1: statistical approaches. Signal Processing 83, 2481–2497 (2003)CrossRefzbMATHGoogle Scholar
  2. 2.
    Markou, M., Singh, S.: Novelty detection: a review - part 2: neural network based approaches. Signal Processing 83, 2499–2521 (2003)CrossRefzbMATHGoogle Scholar
  3. 3.
    Kohonen, T., Oja, E.: Fast Adaptive Formation of Orthogonalizing Filters and Associative Memory in Recurrent Networks of Neuron-Like Elements. Biological Cybernetics 21, 85–95 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Freund, Y., Saphire, R.E.: Experiments with a new boosting algorithm. In: The 13th International Conference on Machine Learning, pp. 276–280 (1996)Google Scholar
  6. 6.
    Kassab, R., Lamirel, J.-C., Nauer, E.: Novelty Detection for Modeling Users Profile. In: The 18th International FLAIRS Conference, pp. 830–831 (2005)Google Scholar
  7. 7.
    Kassab, R., Alexandre, F.: Incremental Data-driven Learning of a Novelty Detection Model for One-Class Classification Problem with Application to High-Dimensional Noisy Data. Machine Learning 74(2), 191–234 (2009)CrossRefGoogle Scholar
  8. 8.
    Breiman, L.: Random forest. Machine Learning (2001)Google Scholar
  9. 9.
    Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. University of California, Irvine (2007)Google Scholar
  10. 10.
    Bradeley, P.W.: The use of area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30, 1145–1159 (1997)CrossRefGoogle Scholar
  11. 11.
    Kubat, M., Holte, R.C., Matwin, S.: Machine Learning for the Detection of Oil Spills in Satellite Radar Images. Machine Learning 30, 195–215 (1998)CrossRefGoogle Scholar
  12. 12.
    Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructures of Cognition, pp. 318–362. MIT Press (1986)Google Scholar
  13. 13.
    Jolliffe, I.T.: Principal Component Analysis. Springer Series in Statistics. Springer, Berlin (1986)CrossRefGoogle Scholar
  14. 14.
    Scholkopf, B., Platt, J., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Computation (1999)Google Scholar
  15. 15.
    Greville, T.N.E.: Some applications of the pseudoinverse of a matrix. SIAM Rev. (1960)Google Scholar
  16. 16.
    Zhang, Y., Burer, S., Street, W.N.: Ensemble prunning via semi-denite programming. Journal of Machin Learning Reasearch 7, 1315–1338 (2006)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Hamdi, F., Bennani, Y.: Learning Random Subspace Detection Filter. In: International Joint Conference in Neural Networks, IJCNN (2011)Google Scholar
  18. 18.
    Caruna, R., Niculescu Mizil, A., Grew, G., Ksikes, A.: Ensemble selection from librairiesof models. In: The 21st International Conference on Machin Learning (2004)Google Scholar
  19. 19.
    Catell, R.: The scree test for the number of factor. Multivariate Behaviorial Research, 245–276 (1966)Google Scholar
  20. 20.
    García, V., Mollineda, R.A., Sánchez, J.S.: Index of balanced accuracy: A performance measure for skewed class distributions. In: Araujo, H., Mendonça, A.M., Pinho, A.J., Torres, M.I. (eds.) IbPRIA 2009. LNCS, vol. 5524, pp. 441–448. Springer, Heidelberg (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Fatma Hamdi
    • 1
  1. 1.CGI BCFrance

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