Classification Model for Data Streams Based on Similarity

  • Dayrelis Mena Torres
  • Jesús Aguilar Ruiz
  • Yanet Rodríguez Sarabia
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6703)


Mining data streams is a field of study that poses new challenges. This research delves into the study of applying different techniques of classification of data streams, and carries out a comparative analysis with a proposal based on similarity; introducing a new form of management of representative data models and policies of insertion and removal, advancing also in the design of appropriate estimators to improve classification performance and updating of the model.


classification data streams similarity 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Orriols-puig, A., Casillas, J., Bernado, E.: Fuzzy-UCS: A Michigan-style Learning Fuzzy-Classifier System for Supervised Learning. Transactions on Evolutionary Computation, 1–23 (2008)Google Scholar
  2. 2.
    Polikar, R., Udpa, L., Udpa, S.S., Honavar, V.: LEARN ++: an Incremental Learning Algorithm For Multilayer Perceptron Networks. IEEE Transactions on System, Man and Cybernetics (C), Special Issue on Knowledge Management, 3414–3417 (2000)Google Scholar
  3. 3.
    Ferrer, F.J., Aguilar, J.S., Riquelme, J.C.: Incremental Rule Learning and Border Examples Selection from Numerical Data Streams. Journal of Universal Computer Science, 1426–1439 (2005)Google Scholar
  4. 4.
    Widmer, G.: Combining Robustness and Flexibility in Learning Drifting Concepts. Machine Learning, 1–11 (1994)Google Scholar
  5. 5.
    Schlimmer, J.C., Granger, R.H.: Incremental learning from noisy data. Machine Learning 1(3), 317–354 (1986)Google Scholar
  6. 6.
    Watanabe, L., Elio, R.: Guiding Constructive Induction for Incremental Learning from Examples. Knowledge Acquisition, 293–296 (1987)Google Scholar
  7. 7.
    Kolter, J.Z., Maloof, M.A.: Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift. In: Proceedings of the Third International IEEE Conference on Data Mining, pp. 123–130 (2003)Google Scholar
  8. 8.
    Domingos, P., Hulten, G.: Mining High-Speed Data Streams. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 71–80 (2000)Google Scholar
  9. 9.
    Jin, R., Agrawal, G.: Efficient Decision Tree Construction on Streaming Data. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 571 – 576(2003)Google Scholar
  10. 10.
    Ramos-jim, G., Jos, R.M.-b., Avila, C.: IADEM-0: Un Nuevo Algoritmo Incremental, pp. 91–98 (2004)Google Scholar
  11. 11.
    Beringer, J., Hullermeier, E.: Efficient Instance-Based Learning on Data Streams. Intelligent Data Analysis, 1–43 (2007)Google Scholar
  12. 12.
    Salganicoff, M.: Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching. Artificial Intelligence Review, 133–155 (1997)Google Scholar
  13. 13.
    Klinkenberg, R., Joachims, T.: Detecting Concept Drift with Support Vector Machines. In: Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pp. 487–494 (2000)Google Scholar
  14. 14.
    Mukherjee, K.: Application of the Gabriel Graph to Instance Based Learning Algorithms. PhD thesis, Simon Fraser University (2004)Google Scholar
  15. 15.
    Aha, D.W., Kibler, D., Albert, M.K.: Instance-Based Learning Algorithms. Machine Learning 66, 37–66 (1991)Google Scholar
  16. 16.
    Randall Wilson, D., Martinez, T.R.: Improved Heterogeneous Distance Functions. Artificial Intelligence 6, 1–34 (1997)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Stanfill, C., Waltz, D.: Toward memory-based reasoning. Communications of the ACM 29(12), 1213–1228 (1986)CrossRefGoogle Scholar
  18. 18.
    Gama, J., Medas, P., Rocha, R.: Forest Trees for On-line Data. In: Proceedings of the 2004 ACM Symposium on Applied Computing, pp. 632–636 (2004)Google Scholar
  19. 19.
    Gama, J., Rocha, R., Medas, P.: Accurate Decision Trees for Mining High-speed Data Streams. In: Proc. SIGKDD, pp. 523–528 (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dayrelis Mena Torres
    • 1
  • Jesús Aguilar Ruiz
    • 2
  • Yanet Rodríguez Sarabia
    • 3
  1. 1.University of Pinar del Río “Hermanos Saíz Montes de Oca”Cuba
  2. 2.University “Pablo de Olavide”Spain
  3. 3.Central University of Las Villas “Marta Abreu”Cuba

Personalised recommendations