Semi-supervised feature selection by minimum neighborhood redundancy and maximum neighborhood relevancy
Author:
Funder
National Science Foundation of China
National College Students’ Innovation and Entrepreneurship Training Plan Program
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10489-024-05578-9.pdf
Reference50 articles.
1. Liu KY, Li TR, Yang XB, Ju HR, Yang X, Liu D (2022) Hierarchical neighborhood entropy based multi-granularity attribute reduction with application to gene prioritization. Int J Approx Reason 148:57–67. https://doi.org/10.1016/j.ijar.2022.05.011
2. Ju HR, Shan TT, Ding WP, Liu KY, Khan MJ, Huang JS, Yang XB (2024) BiFuG2-Spark: bi-directional fuzzy granular-cabin parallel attribute reduction accelerator with granular-group collaboration. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2024.3392328
3. Jiang ZH, Liu KY, Song JJ, Yang XB, Li JH, Qian YH (2021) Accelerator for crosswise computing reduct. Appl Soft Comput 98:106740. https://doi.org/10.1016/j.asoc.2020.106740
4. Xu WH, Yuan KH, Li WT, Ding WP (2023) An emerging fuzzy feature selection method using composite entropy-based uncertainty measure and data distribution. IEEE Trans Emerg Top Comput Intell 7(1):76–88. https://doi.org/10.1109/TETCI.2022.3171784
5. Sang BB, Xu WH, Chen HM, Li TR (2023) Active antinoise fuzzy dominance rough feature selection using adaptive $$k$$-nearest neighbors. IEEE Trans Fuzzy Syst 31(11):3944–3958. https://doi.org/10.1109/TFUZZ.2023.3272316
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