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  1. Pool and accuracy based stream classification: A new ensemble algorithm on data stream classification using recurring concepts detection
Pool and accuracy based stream classification: A new ensemble algorithm on data stream classification using recurring concepts detection
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Pool and accuracy based stream classification: A new ensemble algorithm on data stream classification using recurring concepts detection

Hosseini, M. J.

  1. DOI:10.1109/ICDMW.2011.137
  2. Main Entry: Hosseini, M. J.
  3. Title:Pool and accuracy based stream classification: A new ensemble algorithm on data stream classification using recurring concepts detection.
  4. Abstract:One of the main challenges of data streams is the occurrence of concept drift. Concept drift is the change of target (or feature) distribution, and can occur in different types: sudden, gradual, incremental or recurring. Because of the forgetting mechanism existing in the data stream learning process, recurring concepts has received much attention recently, and became a challenging problem. This paper tries to exploit the existence of recurring concepts in the learning process and improve the classification of data streams. It uses a pool of concepts to detect the reoccurrence of a concept using two methods: a Bayesian, and a heuristic method. Two approaches are used in the classification process: active classifier and weighted classifier. Experimental results show the effectiveness of the proposed method with respect to the Conceptual Clustering and Prediction (CCP) framework
  5. Notes:Sharif Repository
  6. Subject:Concept drift.
  7. Subject:Classification of data.
  8. Subject:Classification process.
  9. Subject:Concept drifts.
  10. Subject:Conceptual clustering.
  11. Subject:Data stream.
  12. Subject:Ensemble algorithms.
  13. Subject:Ensemble learning.
  14. Subject:Learning process.
  15. Subject:Recurring concepts.
  16. Subject:Stream classification.
  17. Subject:Stream mining.
  18. Subject:Data communication systems.
  19. Subject:Heuristic methods.
  20. Subject:Lakes.
  21. Subject:Learning systems.
  22. Subject:Data mining.
  23. Added Entry:Ahmadi, Z.
  24. Added Entry:Beigy, H.
  25. Added Entry:Sharif University of Technology.
  26. Added Entry:11th IEEE International Conference on Data Mining Workshops, ICDMW 2011, Vancouver, BC, 11 December 2011 through 11 December 2011
  27. Added Entry:ICDMW 2011
  28. Source: Proceedings - IEEE International Conference on Data Mining, ICDM, 11 December 2011 through 11 December 2011, Vancouver, BC ; 2011 , Pages 588-595 ; 15504786 (ISSN) ; 9780769544090 (ISBN)
  29. Web Site:http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6137433

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