Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Learning in the presence of concept drift and hidden contexts
Machine Learning
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
ACE: adaptive classifiers-ensemble system for concept-drifting environments
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Combining Time and Space Similarity for Small Size Learning under Concept Drift
ISMIS '09 Proceedings of the 18th International Symposium on Foundations of Intelligent Systems
Learning, detecting, understanding, and predicting concept changes
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Using diversity to handle concept drift in on-line learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
We're not in Kansas anymore: detecting domain changes in streams
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Partial drift detection using a rule induction framework
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Exploiting concept clumping for efficient incremental e-mail categorization
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications - Volume Part II
New drift detection method for data streams
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Pattern change discovery between high dimensional data sets
Proceedings of the 20th ACM international conference on Information and knowledge management
Detecting change via competence model
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Concept drift detection via competence models
Artificial Intelligence
Classifying evolving data streams with partially labeled data
Intelligent Data Analysis
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Detecting concept drift is important for dealing with realworld online learning problems. To detect concept drift in a small number of examples, methods that have an online classifier and monitor its prediction errors during the learning have been developed. We have developed such a detection method that uses a statistical test of equal proportions. Experimental results showed that our method performed well in detecting the concept drift in five synthetic datasets that contained various types of concept drift.