The weighted majority algorithm
Information and Computation
Learning in the presence of concept drift and hidden contexts
Machine Learning
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic integration of classifiers for handling concept drift
Information Fusion
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Reoccurring Context is a phenomenon being subject of interest in machine learning theory dealing with Concept Drift. Periodic reappearance of contexts naturally encourage designing classifier systems which utilizes their expertize on contexts collected in the past. The paper presents study on EAERC algorithm that gather its knowledge on appearing contexts in form of elementary classifiers which can potentially contribute in ensemble classifier system if necessary while keeping ensemble size strictly limited to ensure short response time. While unseen context appears EAERC automatically adds new classifier to the pool.