Feature Set Optimization
Feature Set Optimization is a methodological process in machine learning and data analysis that systematically selects, transforms, or reduces a collection of input features to improve model performance, interpretability, or computational efficiency while preserving essential information relevant to the learning task.
274
Publications
14.2K
Citations
949
Authors
478
Institutions
Publications per year
2017–2026
154
Variants
Feature Optimization
Parents
274
A Branch and Bound Algorithm for Feature Subset Selection
Narendra, Fukunaga · IEEE Transactions on Computers · 1977 · 1.2K citations
Trace ratio criterion for feature selection
Feiping Nie, Shiming Xiang, Yangqing Jia et al. · National University of Singapore · 2008 · 327 citations
An improved particle swarm optimization for feature selection
Yuanning Liu, Gang Wang, Huiling Chen et al. · Journal of Bionic Engineering · 2011 · 323 citations
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478

Chinese Academy of Sciences
Beijing, China
10 h-index
34 publications
889 citations
Islamic Azad University of Najafabad
Najafābād, Iran
5 h-index
12 publications
1.5K citations

Mansoura University
Al Mansurah, Egypt
5 h-index
5 publications
380 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
5 h-index
15 publications
445 citations
Princess Nourah bint Abdulrahman University
Riyadh, Saudi Arabia
5 h-index
8 publications
638 citations
Yonsei University
Seoul, South Korea
5 h-index
5 publications
592 citations
Torrens University Australia
Adelaide, Australia
4 h-index
7 publications
966 citations

Cairo University
Giza, Egypt
4 h-index
5 publications
515 citations
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13 publications · 651 citations
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6 publications · 683 citations
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6 publications · 584 citations
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4 publications · 230 citations
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4 publications · 164 citations
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