Learning in the presence of concept drift and hidden contexts
Machine 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
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning decision trees from dynamic data streams
Proceedings of the 2005 ACM symposium on Applied computing
ACM SIGMOD Record
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Dynamic integration of classifiers for handling concept drift
Information Fusion
Tracking Recurrent Concept Drift in Streaming Data Using Ensemble Classifiers
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Adaptive concept drift detection
Statistical Analysis and Data Mining - Best of SDM'09
Improving Adaptive Bagging Methods for Evolving Data Streams
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
A case-based technique for tracking concept drift in spam filtering
Knowledge-Based Systems
Tracking recurring contexts using ensemble classifiers: an application to email filtering
Knowledge and Information Systems
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
IEEE Transactions on Knowledge and Data Engineering
The Journal of Machine Learning Research
Learning recurring concepts from data streams with a context-aware ensemble
Proceedings of the 2011 ACM Symposium on Applied Computing
Accuracy updated ensemble for data streams with concept drift
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Concept drift and how to identify it
Web Semantics: Science, Services and Agents on the World Wide Web
Exponentially weighted moving average charts for detecting concept drift
Pattern Recognition Letters
Fast perceptron decision tree learning from evolving data streams
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
DDD: A New Ensemble Approach for Dealing with Concept Drift
IEEE Transactions on Knowledge and Data Engineering
Incremental Learning of Concept Drift in Nonstationary Environments
IEEE Transactions on Neural Networks
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This paper presents recurring concept drifts (RCD), a framework that offers an alternative approach to handle data streams that suffer from recurring concept drifts (on-line learning). It creates a new classifier to each context found and stores a sample of data used to build it. When a new concept drift occurs, the algorithm compares the new context to previous ones using a non-parametric multivariate statistical test to verify if both contexts come from the same distribution. If so, the corresponding classifier is reused. The RCD framework is compared with several algorithms (among single and ensemble approaches), in both artificial and real data sets, chosen from frequently used algorithms and data sets in the concept drift research area. We claim the proposed framework had better average ranks in data sets with abrupt and gradual concept drifts compared to both the single classifiers and the ensemble approaches that use the same base learner.