Can cross-company data improve performance in software effort estimation?
Proceedings of the 8th International Conference on Predictive Models in Software Engineering
Drift detection and characterization for fault diagnosis and prognosis of dynamical systems
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
Next challenges for adaptive learning systems
ACM SIGKDD Explorations Newsletter
RCD: A recurring concept drift framework
Pattern Recognition Letters
Challenges and opportunities in dynamic optimisation
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
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Online learning algorithms often have to operate in the presence of concept drifts. A recent study revealed that different diversity levels in an ensemble of learning machines are required in order to maintain high generalization on both old and new concepts. Inspired by this study and based on a further study of diversity with different strategies to deal with drifts, we propose a new online ensemble learning approach called Diversity for Dealing with Drifts (DDD). DDD maintains ensembles with different diversity levels and is able to attain better accuracy than other approaches. Furthermore, it is very robust, outperforming other drift handling approaches in terms of accuracy when there are false positive drift detections. In all the experimental comparisons we have carried out, DDD always performed at least as well as other drift handling approaches under various conditions, with very few exceptions.