Ensembles of Heterogeneous Concept Drift Detectors - Experimental Study

  • Michał WoźniakEmail author
  • Paweł Ksieniewicz
  • Bogusław Cyganek
  • Krzysztof Walkowiak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9842)


For the contemporary enterprises, possibility of appropriate business decision making on the basis of the knowledge hidden in stored data is the critical success factor. Therefore, the decision support software should take into consideration that data usually comes continuously in the form of so-called data stream, but most of the traditional data analysis methods are not ready to efficiently analyze fast growing amount of the stored records. Additionally, one should also consider phenomenon appearing in data stream called concept drift, which means that the parameters of an using model are changing, what could dramatically decrease the analytical model quality. This work is focusing on the classification task, which is very popular in many practical cases as fraud detection, network security, or medical diagnosis. We propose how to detect the changes in the data stream using combined concept drift detection model. The experimental evaluations confirm its pretty good quality, what encourage us to use it in practical applications.


Data stream Concept drift Pattern classification Drift detector 



This work was supported by the statutory funds of the Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology and by the Polish National Science Centre under the grant No. DEC-2013/09/B/ST6/02264. This work was also supported by the AGH Statutory Funds No. All computer experiments were carried out using computer equipment sponsored by ENGINE project (


  1. 1.
    Bifet, A., Holmes, G., Kirkby, R., Pfahringer, B.: MOA: massive online analysis. J. Mach. Learn. Res. 11, 1601–1604 (2010)Google Scholar
  2. 2.
    Bifet, A., Read, J., Pfahringer, B., Holmes, G., Žliobaitė, I.: CD-MOA: change detection framework for massive online analysis. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 92–103. Springer, Heidelberg (2013)zbMATHCrossRefGoogle Scholar
  3. 3.
    Blanco, I.I.F., del Campo-Avila, J., Ramos-Jimenez, G., Bueno, R.M., Diaz, A.A.O., Mota, Y.C.: Online and non-parametric drift detection methods based on Hoeffding’s bounds. IEEE Trans. Knowl. Data Eng. 27(3), 810–823 (2015)CrossRefGoogle Scholar
  4. 4.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley, New York (2001)zbMATHGoogle Scholar
  5. 5.
    Gama, J., Zliobaite, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. 46(4), 44:1–44:37 (2014)zbMATHCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Greiner, R., Grove, A.J., Roth, D.: Learning cost-sensitive active classifiers. Artif. Intell. 139(2), 137–174 (2002)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gustafsson, F.: Adaptive Filtering and Change Detection. Wiley, New York (2000)Google Scholar
  9. 9.
    Harel, M., Mannor, S., El-yaniv, R., Crammer, K.: Concept drift detection through resampling. In: Jebara, T., Xing, E.P. (eds.) Proceedings of the 31st International Conference on Machine Learning (ICML 2014), JMLR Workshop and Conference Proceedings, pp. 1009–1017 (2014)Google Scholar
  10. 10.
    Jackowski, K., Krawczyk, B., Woźniak, M.: Improved adaptive splitting and selection: the hybrid training method of a classifier based on a feature space partitioning. Int. J. Neural Syst. 24(03), 1430007 (2014)CrossRefGoogle Scholar
  11. 11.
    Krawczyk, B.: One-class classifier ensemble pruning and weighting with firefly algorithm. Neurocomputing 150, 490–500 (2015)CrossRefGoogle Scholar
  12. 12.
    Kuncheva, L.I.: Clustering-and-selection model for classifier combination. In: Proceedings of the Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 185–188 (2000)Google Scholar
  13. 13.
    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
  14. 14.
    Kuncheva, L.I.: Combining Pattern Classifiers: Methods and Algorithms. Wiley-Interscience, Hoboken (2004)zbMATHCrossRefGoogle Scholar
  15. 15.
    Lughofer, E., Angelov, P.P.: Handling drifts and shifts in on-line data streams with evolving fuzzy systems. Appl. Soft Comput. 11(2), 2057–2068 (2011)CrossRefGoogle Scholar
  16. 16.
    Ouyang, Z., Gao, Y., Zhao, Z., Wang, T.: Study on the classification of data streams with concept drift. In: FSKD, pp. 1673–1677. IEEE (2011)Google Scholar
  17. 17.
    Page, E.S.: Continuous inspection schemes. Biometrika 41(1/2), 100–115 (1954)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Raudys, S.: Statistical and Neural Classifiers: An Integrated Approach to Design. Springer Publishing Company, London (2014). IncorporatedzbMATHGoogle Scholar
  19. 19.
    Schlimmer, J.C., Granger Jr., R.H.: Incremental learning from noisy data. Mach. Learn. 1(3), 317–354 (1986)Google Scholar
  20. 20.
    Sebastiao, R., Gama, J.: A study on change detection methods. In: Progress in Artificial Intelligence, 14th Portuguese Conference on Artificial Intelligence, EPIA, pp. 12–15 (2009)Google Scholar
  21. 21.
    Sobolewski, P., Wozniak, M.: Concept drift detection and model selection with simulated recurrence and ensembles of statistical detectors. J. Univers. Comput. Sci. 19(4), 462–483 (2013)Google Scholar
  22. 22.
    Widmer, G., Kubat, M.: Effective learning in dynamic environments by explicit context tracking. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol. 667, pp. 227–243. Springer, Heidelberg (1993)CrossRefGoogle Scholar
  23. 23.
    Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Mach. Learn. 23(1), 69–101 (1996)Google Scholar
  24. 24.
    Wozniak, M.: A hybrid decision tree training method using data streams. Knowl. Inf. Syst. 29(2), 335–347 (2011)CrossRefGoogle Scholar
  25. 25.
    Wozniak, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). Special Issue on Information Fusion in Hybrid Intelligent Fusion SystemsCrossRefGoogle Scholar

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Authors and Affiliations

  • Michał Woźniak
    • 1
    Email author
  • Paweł Ksieniewicz
    • 1
  • Bogusław Cyganek
    • 2
  • Krzysztof Walkowiak
    • 1
  1. 1.Department of Systems and Computer Networks, Faculty of ElectronicsWrocław University of TechnologyWrocławPoland
  2. 2.AGH University of Science and TechnologyKrakówPoland

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