Global ETD Search
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Showing 1 to 4 of 4 for “"Drift Detection Methods"”.
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Unsupervised Concept Drift Detection in Data Streams
… stream mining, efficiently detecting concept drifts is still challenging due to the high cost of collecting true class labels. Traditional detection methods usually need high computation and memory cost and is unable to distinguish between concept drift and novelty. To improve the drift …
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A Reservoir of Adaptive Algorithms for Online Learning from Evolving Data Streams
… change in data is known as concept drift. Concept drift may shift decision boundaries, and cause a decline in accuracy. Learning algorithms, indeed, have to detect concept drift in evolving data streams and replace their predictive models accordingly. To address this challenge, …
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Anomalous behaviour detection for cyber defence in modern industrial control systems
… a novel super learner ensemble anomaly detection and cyber risk quantification framework to profile anomalous behaviour in ICS and derive a cyber risk score. The proposed framework and associated learning models are experimentally validated. The produced results are promising and achieve …
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New probabilistic approaches for detecting and evaluating concept drift in data streams
… financial forecasting, and real-time fraud detection, data distributions frequently shift, causing predictive models trained on historical data to underperform. This phenomenon, known as Concept Drift (CD), presents a major challenge in adaptive learning environments, necessitating ongoing …