Cost-sensitive sparse group online learning for imbalanced data streams-Reference-Cited by-同舟云学术

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Cost-sensitive sparse group online learning for imbalanced data streams

Published:2023-11-20 Issue: Volume: Page:
ISSN:0885-6125
Container-title:Machine Learning
language:en
Short-container-title:Mach Learn

Author:

Chen Zhong,Sheng Victor,Edwards Andrea,Zhang Kun^ORCID

Funder

Army Research Office

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

https://link.springer.com/content/pdf/10.1007/s10994-023-06403-z.pdf

Reference44 articles.

1. Barron, J. T. (2019). A general and adaptive Rubost loss function. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, (pp. 4331–4339).

2. Bernardo, A., & Della Valle, E. (2021). Vfc-smote: very fast continuous synthetic minority oversampling for evolving data streams. Data Mining and Knowledge Discovery, 35(6), 2679–2713. https://doi.org/10.1007/s10618-021-00786-0

3. Brzezinski, D., Minku, L. L., Pewinski, T., et al. (2021). The impact of data difficulty factors on classification of imbalanced and concept drifting data streams. Knowledge and Information Systems, 63(6), 1429–1469. https://doi.org/10.1007/s10115-021-01560-w

4. Cano, A., & Krawczyk, B. (2020). Kappa updated ensemble for drifting data stream mining. Machine Learning, 109(10), 175–218. https://doi.org/10.1007/s10994-019-05840-z

5. Cano, A., & Krawczyk, B. (2022). Rose: Robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Machine Learning, 111(7), 2561–2599. https://doi.org/10.1007/s10994-022-06168-x

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