Using diversity to handle concept drift in on-line learning

Fernanda L. MINKU, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

9 Citations (Scopus)


A recent study of diversity using on-line ensembles of learning machines on the presence of concept drift shows that different diversity levels are required before and after a drift. Besides, studies from the dynamic optimisation problems area suggest that, if the best solution for a particular time step is adopted, it may lead to a future scenario in which low accuracy is obtained. Based on that, we propose in this paper a new online ensemble learning approach to handle concept drift, which uses ensembles containing different diversity levels. Even though a high diversity ensemble may have low accuracy while the concept is stable, it may present better accuracy after a drift. The proposed approach successfully chooses the ensemble to be used when a concept drift occurs and shows to obtain better accuracy than a system which adopts the strategy of learning a new classifier from scratch when a drift is detected (strategy adopted by many of the current approaches that explicitly use a drift detection method). © 2009 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
Number of pages8
Publication statusPublished - Jun 2009
Externally publishedYes


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