Online Machine Learning from Non-stationary Data Streams in the Presence of Concept Drift and Class Imbalance: A Systematic Review

• Main Authors: Palli, Abdul Sattar, Jaafar, Jafreezal, Gilal, Abdul Rehman, Alsughayyir, Aeshah, Gomes, Heitor Murilo, Alshanqiti, Abdullah, Omar, Mazni
• Format: Article
• Language: English
• Published: Universiti Utara Malaysia Press 2024
• Subjects: QA75 Electronic computers. Computer science
• Online Access: https://repo.uum.edu.my/id/eprint/30350/1/JICT%2023%2001%202024%20105-139.pdf; https://doi.org/10.32890/jict2024.23.1.5; https://repo.uum.edu.my/id/eprint/30350/; https://e-journal.uum.edu.my/index.php/jict/article/view/20733; https://doi.org/10.32890/jict2024.23.1.5

In IoT environment applications generate continuous non-stationary data streams with in-built problems of concept drift and class imbalance which cause classifier performance degradation. The imbalanced data affects the classifier during concept detection and concept adaptation. In general, for conc...