Learning in the presence of concept drift and hidden contexts
Machine Learning
Tolerating Concept and Sampling Shift in Lazy Learning UsingPrediction Error Context Switching
Artificial Intelligence Review - Special issue on lazy 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
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Systematic data selection to mine concept-drifting data streams
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
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Traditional researches on data stream mining only put emphasis on building classifiers with high accuracy, which always results in classifiers with dramatic drop of accuracy when concept drifts. In this paper, we present our RTRC system that has good classification accuracy when concept drifts and enough samples are scanned in data stream. By using Markov chain and least-square method, the system is able to predict not only on which the next concept is but also on when the concept is to drift. Experimental results confirm the advantages of our system over Weighted Bagging and CVFDT, two representative systems in streaming data mining.