Incremental feature selection based on uncertainty measure for dynamic interval-valued data-Reference-Cited by-同舟云学术

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Incremental feature selection based on uncertainty measure for dynamic interval-valued data

Published:2023-10-24 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Shu Wenhao,Chen Ting,Cao Dongtao,Qian Wenbin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01977-5.pdf

Reference68 articles.

1. Xia S, Zhang H, Li W, Wang G, Giem E, Chen Z (2022) GBNRS: A Novel rough set algorithm for fast adaptive attribute reduction in classification. IEEE Trans Knowl Data Eng 34(3):1231–1242

2. Yang Y, Chen D, Zhang X, Ji ZY (2022) Covering rough set-based incremental feature selection for mixed decision system. Soft Comput 26(6):2651–2669

3. Ma Z, Mi J, Lin Y, Li J (2022) Boundary region-based variable precision covering rough set models. Inform Sci 608:1524–1540

4. Xie J, Hu B, Jiang H (2022) A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets. Int J Approx Reason 144:1–17

5. Sun L, Zhu L, Li W, Zhang C, Balezentis T (2022) Interval-valued functional clustering based on the Wasserstein distance with application to stock data. Inform Sci 606:910–926

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