Enhancing Concept Drift Detection with Simulated Recurrence

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 185)


This paper focuses on the concept drift detection and proposes how to extend the functionality of a statistical concept drift detector for unlabeled observations. For those algorithms the previously developed approach so-called simulated recurrence is implemented as a separate module. It provides information regarding the possible data distribution after concept drift detection. The proposed approaches were compared with five detection algorithms on the basis of computer experiments which were carried ut on the UCI benchmark datasets.


Detection Module Concept Drift Reference Dataset Data Window Multivariate Version 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Newman, D.J., Asuncion, A.: UCI machine learning repository (2007)Google Scholar
  2. 2.
    Dries, A., Rückert, U.: Adaptive concept drift detection. Stat. Anal. Data Min. 2(56), 311–327 (2009)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Friedman, J., Rafsky, L.: Multivariate generalizations of the wald-wolfowitz and smirnov two-sample tests. The Annals of Statistics, 697–717 (1979)Google Scholar
  4. 4.
    Gama, J., Medas, P., Castillo, G., Rodrigues, P.: Learning with Drift Detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286–295. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Greiner, R., Grove, A.J., Roth, D.: Learning cost-sensitive active classifiers. Artif. Intell. 139(2), 137–174 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hotelling, H.: The Generalization of Student’s Ratio. Annals of Mathematical Statistics 2(3), 360–378 (1931)MATHCrossRefGoogle Scholar
  7. 7.
    Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, pp. 97–106 (2001)Google Scholar
  8. 8.
    Kuncheva, L.I.: Classifier Ensembles for Changing Environments. In: Roli, F., Kittler, J., Windeatt, T. (eds.) MCS 2004. LNCS, vol. 3077, pp. 1–15. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. 9.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 4th edn. Chapman & Hall/CRC (2007)Google Scholar
  10. 10.
    Sobolewski, P., Woźniak, M.: Artificial Recurrence for Classification of Streaming Data with Concept Shift. In: Bouchachia, A. (ed.) ICAIS 2011. LNCS, vol. 6943, pp. 76–87. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Sobolewski, P., Woźniak, M.: Data with Shifting Concept Classification Using Simulated Recurrence. In: Pan, J.-S., Chen, S.-M., Nguyen, N.T. (eds.) ACIIDS 2012, Part I. LNCS, vol. 7196, pp. 403–412. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  12. 12.
    Vreeken, J., van Leeuwen, M., Siebes, A.: Characterising the difference. In: Berkhin, P., Caruana, R., Wu, X. (eds.) KDD, pp. 765–774. ACM (2007)Google Scholar
  13. 13.
    Zliobaite, I.: Change with delayed labeling: When is it detectable? In: Proceedings of the 2010 IEEE International Conference on Data Mining Workshops, ICDMW 2010, pp. 843–850. IEEE Computer Society, Washington, DC (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Systems and Computer Networks, Faculty of ElectronicsWroclaw University of TechnologyWroclawPoland

Personalised recommendations