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The Scientific World Journal
Volume 2015, Article ID 235810, 14 pages
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.


The treatment of large data streams in the presence of concept drifts is one of the main challenges in the field of data mining, particularly when the algorithms have to deal with concepts that disappear and then reappear. This paper presents a new algorithm, called Fast Adapting Ensemble (FAE), which adapts very quickly to both abrupt and gradual concept drifts, and has been specifically designed to deal with recurring concepts. FAE processes the learning examples in blocks of the same size, but it does not have to wait for the batch to be complete in order to adapt its base classification mechanism. FAE incorporates a drift detector to improve the handling of abrupt concept drifts and stores a set of inactive classifiers that represent old concepts, which are activated very quickly when these concepts reappear. We compare our new algorithm with various well-known learning algorithms, taking into account, common benchmark datasets. The experiments show promising results from the proposed algorithm (regarding accuracy and runtime), handling different types of concept drifts.