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The Scientific World Journal
Volume 2014, Article ID 497354, 11 pages
Research Article

A Dynamic Ensemble Framework for Mining Textual Streams with Class Imbalance

Shenzhen Key Laboratory of Internet Information Collaboration, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, China

Received 6 December 2013; Accepted 19 February 2014; Published 10 April 2014

Academic Editors: Z. Chen and F. Yu

Copyright © 2014 Ge Song and Yunming Ye. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [2 citations]

The following is the list of published articles that have cited the current article.

  • Ge Song, Yunming Ye, Haijun Zhang, Xiaofei Xu, Raymond Y. K. Lau, and Feng Liu, “Dynamic Clustering Forest: An ensemble framework to efficiently classify textual data stream with concept drift,” Information Sciences, vol. 357, pp. 125–143, 2016. View at Publisher · View at Google Scholar
  • N. Anupama, and Sudarson Jena, “A novel approach using incremental oversampling for data stream mining,” Evolving Systems, 2018. View at Publisher · View at Google Scholar