Neighbourhood discernibility degree-based semisupervised feature selection for partially labelled mixed-type data with granular ball-Reference-Cited by-同舟云学术

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Neighbourhood discernibility degree-based semisupervised feature selection for partially labelled mixed-type data with granular ball

Published:2023-06-29 Issue:19 Volume:53 Page:22467-22487
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Shu Wenhao,Yu Jianhui,Chen Ting,Qian Wenbin

Funder

National Natural Science Foundation of China

Natural Science Foundation of Jiangxi Province

Key Technologies Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-023-04657-7.pdf

Reference44 articles.

1. Khaire UM, Dhanalakshmi R (2022) Stability of feature selection algorithm: A review. Journal of King Saud University - Computer and Information Sciences 34(4):1060–1073

2. Li X, Wang Y et al (2020) A Survey on Sparse Learning Models for Feature Selection. IEEE Transactions on Cybernetics 52(3):1642–1660

3. Hancer E, Xue B et al (2022) Fuzzy filter cost-sensitive feature selection with differential evolution. Knowl-Based Syst 241:108259

4. Huang P, Yang X (2022) Unsupervised feature selection via adaptive graph and dependency score. Pattern Recogn 127:108622

5. Hja B, Bao Q (2022) On (O, G)-fuzzy rough sets based on overlap and grouping functions over complete lattices. Int J Approximate Reasoning 144:18–50

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