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Statistical Drift Detection Ensemble for batch processing of data streams
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AI
DOI：10.1016/j.knosys.2022.109380.png)
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摘要
En 中文
Among the difficulties being considered in data stream processing, a particularly interesting one is the phenomenon of concept drift. Methods of concept drift detection are frequently used to eliminate the negative impact on the quality of classification in the environment of evolving concepts. This article proposes Statistical Drift Detection Ensemble (SDDE), a novel method of concept drift detection. The method uses drift magnitude and conditioned marginal covariate drift measures, analyzed by an ensemble of detectors, whose members focus on random subspaces of the stream's features. The proposed detector was compared with state-of-the-art methods on both synthetic data streams and the semi-synthetic streams generated based on the real-world concepts. A series of computer experiments and a statistical analysis of the results, both for the classification accuracy and Drift Detection errors were carried out and confirmed the effectiveness of the proposed method. (c) 2022 Published by Elsevier B.V.
Keyword:
Data streams
Concept drift
Drift detection
Statistical drift detection
Classification
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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