Incremental Weighted Ensemble for Data Streams with Concept Drift-Reference-Cited by-同舟云学术

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Incremental Weighted Ensemble for Data Streams with Concept Drift

Published:2022 Issue: Volume: Page:1-12
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Jiao Botao¹^ORCID,Guo Yinan¹^ORCID,Yang Cuie²^ORCID,Pu Jiayang³,Zheng Zhiji¹,Gong Dunwei¹^ORCID

Affiliation:

1. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China

2. School of Engineering and Computer, Victoria University of Wellington, New Zealand

3. Electromechanical and Information Engineering, China University of Mining and Technology (Beijing), Beijing, China

Funder

National Natural Science Foundation of China

Six Talent Peak Project in Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Link

http://xplorestaging.ieee.org/ielx7/9078688/9184921/09961859.pdf?arnumber=9961859

Cited by 9 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Concept Drift Detection Algorithm for Power Data Stream;2024 6th International Conference on Energy Systems and Electrical Power (ICESEP);2024-06-21

2. Adaptive tree-like neural network: Overcoming catastrophic forgetting to classify streaming data with concept drifts;Knowledge-Based Systems;2024-06

3. An Ensemble-Based Informational Fast Resampling Method for Multiclass Imbalanced Data Stream Classification;2024 36th Chinese Control and Decision Conference (CCDC);2024-05-25

4. TS-DM: A Time Segmentation-Based Data Stream Learning Method for Concept Drift Adaptation;IEEE Transactions on Cybernetics;2024

5. An Optimization Method Based on Drift Data and Time Series Information;Lecture Notes in Computer Science;2024

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