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New ensemble method for classification of data streams

Sobhani, P ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/ICCKE.2011.6413362
  3. Abstract:
  4. Classification of data streams has become an important area of data mining, as the number of applications facing these challenges increases. In this paper, we propose a new ensemble learning method for data stream classification in presence of concept drift. Our method is capable of detecting changes and adapting to new concepts which appears in the stream
  5. Keywords:
  6. Boosting ; Concept drift ; Data stream classification ; Ensemble learning ; Classification of data ; Concept drifts ; Data stream classifications ; Ensemble learning ; Ensemble methods ; Data communication systems ; Knowledge engineering ; Data mining
  7. Source: 2011 1st International eConference on Computer and Knowledge Engineering, ICCKE 2011, Mashhad, 13 October 2011 through 14 October 2011 ; 2011 , Pages 264-269 ; 9781467357135 (ISBN)
  8. URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6413362