Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding and Using Context
Personal and Ubiquitous Computing
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards a Theory of Context Spaces
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Tracking Recurring Concepts with Meta-learners
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
RCD: A recurring concept drift framework
Pattern Recognition Letters
Stream-based event prediction using bayesian and bloom filters
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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The dynamic and unstable nature observed in real world applications influences learning systems through changes in data, context and resource availability. Data stream mining systems must be aware and adapt to such changes so that incoming data can continuously be classified with high accuracy. Ensemble approaches have been shown successful in dealing with concept changes. Despite their success in learning under concept changes, context information has not yet been exploited by ensemble approaches in data stream scenarios where concepts reappear. Under these circumstances, context information appropriately integrated with learned concepts would enable to anticipate recurring changes in concepts. In this work, we present an ensemble based approach for the problem of detecting concept changes in data streams where concepts reappear, that dynamically adds and removes weighted classifiers in response to changes not only in concepts but to context. We identify stable concepts using a change detection method, based on the error-rate of the learning process. Context information is used in the adaptation to recurring concepts and in the management of knowledge from previous learned concepts while adapting to resource constraints. Consequently, proper representation and storage of context and concepts is a major issue dealt within the paper. We present and discuss preliminary experimental results with synthetic and real datasets.