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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 874032, 10 pages
http://dx.doi.org/10.1155/2015/874032
Research Article

Adaptive Ensemble with Human Memorizing Characteristics for Data Stream Mining

1State Key Laboratory of High Performance Computing, National University of Defense Technology, Changsha 410073, China
2School of Computer Science, National University of Defense Technology, Changsha 410073, China
3School of Computer and Information Engineering, Hunan University of Commerce, Changsha 410205, China

Received 9 January 2015; Accepted 18 May 2015

Academic Editor: Haibo He

Copyright © 2015 Yanhuang Jiang 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.

Abstract

Combining several classifiers on sequential chunks of training instances is a popular strategy for data stream mining with concept drifts. This paper introduces human recalling and forgetting mechanisms into a data stream mining system and proposes a Memorizing Based Data Stream Mining (MDSM) model. In this model, each component classifier is regarded as a piece of knowledge that a human obtains through learning some materials and has a memory retention value reflecting its usefulness in the history. The classifiers with high memory retention values are reserved in a “knowledge repository.” When a new data chunk comes, most useful classifiers will be selected (recalled) from the repository and compose the current target ensemble. Based on MDSM, we put forward a new algorithm, MAE (Memorizing Based Adaptive Ensemble), which uses Ebbinghaus forgetting curve as the forgetting mechanism and adopts ensemble pruning as the recalling mechanism. Compared with four popular data stream mining approaches on the datasets with different concept drifts, the experimental results show that MAE achieves high and stable predicting accuracy, especially for the applications with recurring or complex concept drifts. The results also prove the effectiveness of MDSM model.