Robust Feature Selection
Robust Feature Selection is a feature selection methodology that systematically identifies and retains informative features under varying data conditions, model assumptions, or perturbations, thereby distinguishing itself from conventional feature selection techniques by its resilience to instability and sensitivity in dynamic or noisy environments.
523
Publications
31.8K
Citations
1.8K
Authors
737
Institutions
Publications per year
2017–2026
275
Parents
523
Feature selection in machine learning: A new perspective
Jie Cai, Jiawei Luo, Shulin Wang et al. · Neurocomputing · 2018 · 2.1K citations
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
Feiping Nie, Heng Huang, Xiao Cai et al. · 2010 · 1.6K citations
1–5 of 523
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737

Southwest Jiaotong University
Chengdu, China
15 h-index
58 publications
2.6K citations

Chinese Academy of Sciences
Beijing, China
11 h-index
28 publications
1.5K citations

Sichuan University
Chengdu, China
9 h-index
15 publications
578 citations
Southwest University
Chongqing, China
9 h-index
15 publications
681 citations
Nanjing University of Science and Technology
Nanjing, China
8 h-index
18 publications
898 citations
Jilin University
Changchun, China
8 h-index
24 publications
1.5K citations
Nanyang Technological University
Singapore, Singapore
8 h-index
12 publications
740 citations
Florida Atlantic University
Boca Raton, United States
7 h-index
18 publications
358 citations
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20 publications · 1.3K citations
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15 publications · 3K citations
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17 publications · 904 citations
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15 publications · 692 citations
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12 publications · 565 citations
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