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

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 con...

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主要な著者:	Abdul Sattar Palli, Jafreezal Jaafar, Abdul Rehman Gilal, Aeshah Alsughayyir, Heitor Murilo Gomes, Abdullah Alshanqiti, Mazni Omar
フォーマット:	Artigo
言語:	Inglês
出版事項:	UUM Press 2024-01-01
シリーズ:	Journal of ICT
主題:	Concept Adaptation Concept Drift Class Imbalance Data Streams Non-stationary
オンライン･アクセス:	https://e-journal.uum.edu.my/index.php/jict/article/view/20733
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