A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning

A Comparison of Approaches for Handling Concept Drifts in Data Processed With Machine Learning

In the realm of machine learning models, the pursuit of achieving favorable metrics is undeniably significant. However, these models confront phenomena that can diminish their effectiveness if left unaddressed-notably, the phenomenon of concept drift. Concept drift materializes when unforeseen alter...

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Bibliographic Details
Main Authors: Emanuel Valerio Pereira, Wendley Souza da Silva
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Classifiers
concept drift
data stream
evaluation
machine learning
Online Access:https://ieeexplore.ieee.org/document/10947750/
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