Feature selections based on two-type overlap degrees and three-view granulation measures for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si376.svg" display="inline" id="d1e14393"><mml:mi>k</mml:mi></mml:math>-nearest-neighbor rough sets-Reference-Cited by-同舟云学术

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Feature selections based on two-type overlap degrees and three-view granulation measures for k-nearest-neighbor rough sets

Published:2024-12 Issue: Volume:156 Page:110837
ISSN:0031-3203
Container-title:Pattern Recognition
language:en
Short-container-title:Pattern Recognition

Author:

Chen Jiang,Zhang Xianyong^ORCID,Yuan Zhong^ORCID

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference34 articles.

1. Neighborhood rough set based heterogeneous feature subset selection;Hu;Inform. Sci.,2008

2. Feature selection with missing labels using multilabel fuzzy neighborhood rough sets and maximum relevance minimum redundancy;Sun;IEEE Trans. Fuzzy Syst.,2022

3. A novel approach to attribute reduction based on weighted neighborhood rough sets;Hu;Knowl.-Based Syst.,2021

4. A novel method to attribute reduction based on weighted neighborhood probabilistic rough sets;Xie;Internat. J. Approx. Reason.,2022

5. GBRS: A unified granular-ball learning model of pawlak rough set and neighborhood rough set;Xia;IEEE Trans. Neural Netw. Learn. Syst.,2024

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