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
Control chart tests based on geometric moving averages
Technometrics
Detecting Concept Drift with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Adaptive threshold computation for CUSUM-type procedures in change detection and isolation problems
Computational Statistics & Data Analysis
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Adaptive Learning Rate for Online Linear Discriminant Classifiers
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Paired Learners for Concept Drift
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
RCD: A recurring concept drift framework
Pattern Recognition Letters
Timeline adaptation for text classification
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
Pattern Recognition Letters
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Classifying streaming data requires the development of methods which are computationally efficient and able to cope with changes in the underlying distribution of the stream, a phenomenon known in the literature as concept drift. We propose a new method for detecting concept drift which uses an exponentially weighted moving average (EWMA) chart to monitor the misclassification rate of an streaming classifier. Our approach is modular and can hence be run in parallel with any underlying classifier to provide an additional layer of concept drift detection. Moreover our method is computationally efficient with overhead O(1) and works in a fully online manner with no need to store data points in memory. Unlike many existing approaches to concept drift detection, our method allows the rate of false positive detections to be controlled and kept constant over time.