Roadmap of Concept Drift Adaptation in Data Stream Mining, Years Later
Osama A. Mahdi,
Nawfal Ali,
Eric Pardede
et al.
Abstract:As machine learning models are increasingly applied to real-world scenarios, it is essential to consider the possibility of changes in the data distribution over time. Concept drift detection and adaptation refers to the process of identifying and tracking these changes and updating the model accordingly.Researchers have devoted significant efforts to develop various techniques and tools for concept drift detection and adaptation, as this paper provides a generic roadmap and review of the field. In this paper,… Show more
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