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The Scientific World Journal
Volume 2015 (2015), Article ID 235810, 14 pages
http://dx.doi.org/10.1155/2015/235810
Research Article

Fast Adapting Ensemble: A New Algorithm for Mining Data Streams with Concept Drift

1Department of Computer Science, University of Granma, 85100 Granma, Cuba
2Department of Language and Computer Science, University of Málaga, Complejo Tecnológico, 29071 Málaga, Spain
3Department of Computer Science, University of Camagüey, 70100 Camagüey, Cuba

Received 26 June 2014; Accepted 15 September 2014

Academic Editor: Shifei Ding

Copyright © 2015 Agustín Ortíz Díaz et al. 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 [4 citations]

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

  • Isvani Frías-Blanco, José del Campo-Ávila, Gonzalo Ramos-Jiménez, AndreC.P.L.F. Carvalho, Agustín Ortiz-Díaz, and Rafael Morales-Bueno, “Online Adaptive Decision Trees Based on Concentration Inequalities,” Knowledge-Based Systems, 2016. View at Publisher · View at Google Scholar
  • Shuliang Xu, and Junhong Wang, “A fast incremental extreme learning machine algorithm for data streams classification,” Expert Systems with Applications, vol. 65, pp. 332–344, 2016. View at Publisher · View at Google Scholar
  • Jean Paul Barddal, Heitor Murilo Gomes, And Fabricio Enembreck, and Albert Bifet, “A survey on ensemble learning for data stream classification,” ACM Computing Surveys, vol. 50, no. 2, 2017. View at Publisher · View at Google Scholar
  • Szu-Yin Lin, Yao-Ching Chiu, Jacek Lewandowski, and Kuo-Ming Chao, “Parallel dynamic data-driven model for concept drift detection and prediction,” Journal of Intelligent and Fuzzy Systems, vol. 32, no. 2, pp. 1413–1426, 2017. View at Publisher · View at Google Scholar