The Research on Tracking Concept Drift Based on Genetic Algorithm

  • Zhong Zhishui
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 115)


Concept drift is a crucial problem in machine learning. The machine learning model, such as classifier, should adapt to the concept change rapidly to keep the precision. At present, most methods for solving this problem is based on the mechanism, which is referred to as Time –Weight. However, current existing methods can not quickly adapt to the concepts, which reappear. Therefore, a method with the ability to adapt concept changing all the time is necessary, especially for the situation of time precious, such as data stream classification. In this paper, we proposed a method of tracking concept drift based on genetic algorithm (TCDGA). We experimented with TCDGA on public dataset and made comparisons. Our results show that TCDGA can yield better performance when concepts reappear.


machine learning concept drift genetic algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: An ensemble method for drifting concepts. Journal of Machine Learning Research 8, 2755–2790 (2007)MATHGoogle Scholar
  2. 2.
    Scholz, M., Klinkenberg, R.: Boosting Classifiers for Drifting Concepts. Intelligent Data Analysis (IDA), Special Issue on Knowledge Discovery from Data Streams 11(1), 3–28 (2007)Google Scholar
  3. 3.
    Widmer, G.: Learning in Dynamically Changing Domains: Recent Contributions of Machine Learning. In: Proceedings of the MLNet Workshop on Learning in Dynamically Changing Domains: Theory Revision and Context Dependence Issues, Czech Republic, Prague (1997)Google Scholar
  4. 4.
    Learning drifting concepts: Example selection vs. example weightingGoogle Scholar
  5. 5.
    Klinkenberg, R., Renz, I.: Learning for Text Categorization, Adaptive information filtering: Learning in the presence of concept drifts (1998)Google Scholar
  6. 6.
    Maloof, M.A., Michalski, R.S.: Selecting examples for partial memory learning. Machine Learning 41(11), 27–52 (2000)CrossRefGoogle Scholar
  7. 7.
    Mitchell, T., Caruana, R., Freitag, D., McDermott, J., Zabowski, D.: Experience with a Learning Personal Assistant. Communications of the ACM 37(7), 81–91 (1994)CrossRefGoogle Scholar
  8. 8.
    Gradual forgetting for adaptation to concept driftGoogle Scholar
  9. 9.
    Schlimmer, J., Granger, R.: Beyond Incremental Processing: Tracking Concept Drift. AAAI (1986)Google Scholar
  10. 10.
    Wang, H., Fan, W., Yu, P.S., Han, J.: Mining concept-drifting data streams using ensemble classifiers. In: KDD (2003)Google Scholar
  11. 11.
    Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69–101 (1996)Google Scholar
  12. 12.
    An introduction to genetic algorithmsGoogle Scholar
  13. 13.
    Learning with Genetic Algorithms: An OverviewGoogle Scholar
  14. 14.
    Development of Genetic-based Machine Learning for Network Intrusion DetectionGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Mathematics and Computer ScienceTongling UniversityTonglingChina

Personalised recommendations