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Communication dans un congrès

Autoregressive based Drift Detection Method

Mansour Zoubeirou a Mayaki 1 Michel Riveill 1 
1 MAASAI - Modèles et algorithmes pour l’intelligence artificielle
CRISAM - Inria Sophia Antipolis - Méditerranée , UNS - Université Nice Sophia Antipolis (1965 - 2019), JAD - Laboratoire Jean Alexandre Dieudonné, Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : In the classic machine learning framework, models are trained on historical data and used to predict future values. It is assumed that the data distribution does not change over time (stationarity). However, in real-world scenarios, the data generation process changes over time and the model has to adapt to the new incoming data. This phenomenon is known as concept drift and leads to a decrease in the predictive model's performance. In this study, we propose a new concept drift detection method based on autoregressive models called ADDM. This method can be integrated into any machine learning algorithm from deep neural networks to simple linear regression model. Our results show that this new concept drift detection method outperforms the state-of-the-art drift detection methods, both on synthetic data sets and real-world data sets. Our approach is theoretically guaranteed as well as empirical and effective for the detection of various concept drifts. In addition to the drift detector, we proposed a new method of concept drift adaptation based on the severity of the drift.
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https://hal.archives-ouvertes.fr/hal-03740180
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Soumis le : vendredi 29 juillet 2022 - 11:25:22
Dernière modification le : jeudi 4 août 2022 - 17:00:06

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Mansour Zoubeirou a Mayaki, Michel Riveill. Autoregressive based Drift Detection Method. IEEE WCCI 2022 - IEEE world congress on computational intelligenceWORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE, Jul 2022, Padoue, Italy. ⟨hal-03740180⟩

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