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
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A semi-random multiple decision-tree algorithm for mining data streams
Journal of Computer Science and Technology
Concept Drifting Detection on Noisy Streaming Data in Random Ensemble Decision Trees
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Random ensemble decision trees for learning concept-drifting data streams
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
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Classification with concept-drifting data streams has found wide applications. However, many classification algorithms on streaming data have been designed for fixed features of concept drift and cannot deal with the noise impact on concept drift detection. An incremental algorithm with Multiple Semi- Random decision Trees (MSRT) for concept-drifting data streams is presented in this paper, which takes two sliding windows for training and testing, uses the inequality of Hoeffding Bounds to determine the thresholds for distinguishing the true drift from noise, and chooses the classification function to estimate the error rate for periodic concept-drift detection. Our extensive empirical study shows that MSRT has an improved performance in time, accuracy and robustness in comparison with CVFDT, a state-of-the-art decision-tree algorithm for classifying concept-drifting data streams.