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• W4220851938 endingPage "108632" @default.
• W4220851938 startingPage "108632" @default.
• W4220851938 abstract "The dynamicity of real-world systems poses a significant challenge to deployed predictive machine learning (ML) models. Changes in the system on which the ML model has been trained may lead to performance degradation during the system’s life cycle. Recent advances that study non-stationary environments have mainly focused on identifying and addressing such changes caused by a phenomenon called concept drift. Different terms have been used in the literature to refer to the same type of concept drift and the same term for various types. This lack of unified terminology is set out to create confusion on distinguishing between different concept drift variants. In this paper, we start by grouping concept drift types by their mathematical definitions and survey the different terms used in the literature to build a consolidated taxonomy of the field. We also review and classify performance-based concept drift detection methods proposed in the last decade. These methods utilize the predictive model’s performance degradation to signal substantial changes in the systems. The classification is outlined in a hierarchical diagram to provide an orderly navigation between the methods. We present a comprehensive analysis of the main attributes and strategies for tracking and evaluating the model’s performance in the predictive system. The paper concludes by discussing open research challenges and possible research directions." @default.
• W4220851938 created "2022-04-03" @default.
• W4220851938 creator A5024836775 @default.
• W4220851938 creator A5031391282 @default.
• W4220851938 creator A5071429505 @default.
• W4220851938 date "2022-06-01" @default.
• W4220851938 modified "2023-10-14" @default.
• W4220851938 title "From concept drift to model degradation: An overview on performance-aware drift detectors" @default.
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