The weighted majority algorithm
Information and Computation
Machine Learning
Decision Tree Induction Based on Efficient Tree Restructuring
Machine Learning
Experimental comparisons of online and batch versions of bagging and boosting
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Online Ensemble Learning: An Empirical Study
Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Using additive expert ensembles to cope with concept drift
ICML '05 Proceedings of the 22nd international conference on Machine learning
A framework for generating data to simulate changing environments
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Negative correlation in incremental learning
Natural Computing: an international journal
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Detecting concept drift using statistical testing
DS'07 Proceedings of the 10th international conference on Discovery science
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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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 on-line 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).