Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
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
Experimental comparisons of online and batch versions of bagging and boosting
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
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 2004 ACM symposium on Applied computing
Categorizing and mining concept drifting data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving the performance of an incremental algorithm driven by error margins
Intelligent Data Analysis - Knowledge Discovery from Data Streams
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
The Journal of Machine Learning Research
Ensembles of Restricted Hoeffding Trees
ACM Transactions on Intelligent Systems and Technology (TIST)
An analysis of change trends by predicting from a data stream using genetic fuzzy systems
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
RCD: A recurring concept drift framework
Pattern Recognition Letters
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We propose two new improvements for bagging methods on evolving data streams. Recently, two new variants of Bagging were proposed: ADWIN Bagging and Adaptive-Size Hoeffding Tree (ASHT) Bagging. ASHT Bagging uses trees of different sizes, and ADWIN Bagging uses ADWIN as a change detector to decide when to discard underperforming ensemble members. We improve ADWIN Bagging using Hoeffding Adaptive Trees, trees that can adaptively learn from data streams that change over time. To speed up the time for adapting to change of Adaptive-Size Hoeffding Tree (ASHT) Bagging, we add an error change detector for each classifier. We test our improvements by performing an evaluation study on synthetic and real-world datasets comprising up to ten million examples.