Exponentially weighted moving average charts for detecting concept drift

By Gordon J. Ross, Niall M. Adams, Dimitris K. Tasoulis, David J. Hand. Pattern Recognition Letters, 2012

412 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/f9f77b317be46ef3429a9086810baef03ce739bb

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In Artificial Intelligence

Citations in the corpus

Learning under Concept Drift: A Review (2019, background): “The implementation of Stages 1-3 of ECDD is the same as for DDM, while Stage 4 is different.”
From Concept Drift to Model Degradation: An Overview on Performance-Aware Drift Detectors (2022, methodology): “Hoeffding Drift Detection Method (HDDM) [97] modifies DDM by using the Hoeffding’s inequality [98] to detect substantial changes in the moving average of the 19 Concept drift detection Windowing technique ADWIN SEED ADWIN2 MDDM ADDM MD3 fsNB EDIST ADDS EDIST2 STEPD FPDD WSTD FSDD CSDD Nacre CALMID S”
Distribution-Free One-Pass Learning (2017, methodology): “We adopt the forgetting mechanism [26], [27], [28], [29].”
Online and Non-Parametric Drift Detection Methods Based on Hoeffding’s Bounds (2015, background): “To improve the detection on gradual and slow types of change, Baena et al. [4] consider the distance between two classification errors instead of considering only the number of errors.”
Fraud detection system: A survey (2016, methodology): “For example, Sahin et al. (2013) used cost-sensitive classifiers in addressing the class imbalance problem.”