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
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Exploiting Context When Learning to Classify
ECML '93 Proceedings of the European Conference on Machine Learning
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International 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
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
Data Mining
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Human-Computer Interaction
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
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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The problem of recurring concepts in data stream classification is a special case of concept drift where concepts may reappear. Although several existing methods are able to learn in the presence of concept drift, few consider contextual information when tracking recurring concepts. Nevertheless, in many real-world scenarios context information is available and can be exploited to improve existing approaches in the detection or even anticipation of recurring concepts. In this work, we propose the extension of existing approaches to deal with the problem of recurring concepts by reusing previously learned decision models in situations where concepts reappear. The different underlying concepts are identified using an existing drift detection method, based on the error-rate of the learning process. A method to associate context information and learned decision models is proposed to improve the adaptation to recurring concepts. The method also addresses the challenge of retrieving the most appropriate concept for a particular context. Finally, to deal with situations of memory scarcity, an intelligent strategy to discard models is proposed. The experiments conducted so far, using synthetic and real datasets, show promising results and make it possible to analyze the trade-off between the accuracy gains and the learned models storage cost.