Symbolic Feature Engineering
Symbolic Feature Engineering is a methodological contribution in machine learning that, as a form of feature engineering, specifically involves the systematic derivation of new features through symbolic manipulation of polynomial and interaction terms to enhance model expressiveness and interpretability.
37
Publications
2.4K
Citations
105
Authors
59
Institutions
Publications per year
2017–2026
14
Parents
37
Kolmogorov-Arnold Networks Meet Science
Physical Review X · 2025 · 145 citations · Full text
Computing, Computer Science, Physics +15
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59
University of Mysore
Mysuru, India
4 h-index
8 publications
112 citations

Universidade Federal de Pernambuco
Recife, Brazil
2 h-index
5 publications
334 citations

Johns Hopkins University
Baltimore, United States
2 h-index
4 publications
2.1K citations

Pennsylvania State University
State College, United States
2 h-index
10 publications
335 citations
Johannes Gutenberg University Mainz
Mainz, Germany
1 h-index
1 publications
15 citations

Osnabrück University
Osnabrück, Germany
1 h-index
1 publications
45 citations

Delft University of Technology
Delft, The Netherlands
1 h-index
1 publications
32 citations
Euskadiko Parke Teknologikoa
Derio, Spain
1 h-index
1 publications
33 citations
1–8 of 59
3 publications · 135 citations
3
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2 publications · 51 citations
2
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2 publications · 97 citations
2
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2 publications · 1K citations
2
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1 publications · 114 citations
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1–5