Maximum relevance minimum redundancy-based feature selection using rough mutual information in adaptive neighborhood rough sets
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Artificial Intelligence
Link
https://link.springer.com/content/pdf/10.1007/s10489-022-04398-z.pdf
Reference63 articles.
1. Xu WH, Yuan KH, Li WT (2022) Dynamic updating approximations of local generalized multigranulation neighborhood rough set. Appl Intell 52(8):9148–9173
2. Lu HH, Chen HM, Li TR, Chen H, Luo C (2022) Multi-label feature selection based on manifold regularization and imbalance ratio. Appl Intell 52(10):11652–11671
3. Yang XL (2021) Neighborhood rough sets with distance metric learning for feature selection. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2021.107076, Li TR
4. Ibrahim RA, Abd Elaziz M, Oliva D (2020) An improved runner-root algorithm for solving feature selection problems based on rough sets and neighborhood rough sets. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2019.105517
5. Wan JH, Chen HM, Yuan Z (2021) A novel hybrid feature selection method considering feature interaction in neighborhood rough set. Knowl-Based Syst. https://doi.org/10.1016/j.knosys.2021.107167https://doi.org/10.1016/j.knosys.2021.107167
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