Resample-Based Ensemble Framework for Drifting Imbalanced Data Streams

Machine learning in real-world scenarios is often challenged by concept drift and class imbalance. This paper proposes a Resample-based Ensemble Framework for Drifting Imbalanced Stream (RE-DI). The ensemble framework consists of a long-term static classifier to handle gradual and multiple dynamic c...

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Κύριοι συγγραφείς:	Hang Zhang, Weike Liu, Shuo Wang, Jicheng Shan, Qingbao Liu
Μορφή:	Artigo
Γλώσσα:	Inglês
Έκδοση:	IEEE 2019-01-01
Σειρά:	IEEE Access
Θέματα:	Online ensemble learning resample learning reinforcement concept drift class imbalance
Διαθέσιμο Online:	https://ieeexplore.ieee.org/document/8706959/
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