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
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
Mining in Anticipation for Concept Change: Proactive-Reactive Prediction in Data Streams
Data Mining and Knowledge Discovery
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
Human-Computer Interaction
Towards Context Aware Food Sales Prediction
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
Novel data stream pattern mining report on the StreamKDD'10 workshop
ACM SIGKDD Explorations Newsletter
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Drift detection methods in data streams can detect changes in incoming data so that learned models can be used to represent the underlying population. In many real-world scenarios context information is available and could be exploited to improve existing approaches, by detecting or even anticipating to recurring concepts in the underlying population. Several applications, among them health-care or recommender systems, lend themselves to use such information as data from sensors is available but is not being used. Nevertheless, new challenges arise when integrating context with drift detection methods. Modeling and comparing context information, representing the context-concepts history and storing previously learned concepts for reuse are some of the critical problems. In this work, we propose the Context-aware Learning from Data Streams (CALDS) system to improve existing drift detection methods by exploiting available context information. Our enhancement is seamless: we use the association between context information and learned concepts to improve detection and adaptation to drift when concepts reappear. We present and discuss our preliminary experimental results with synthetic and real datasets.