Feature Engineering for Tabular Data is a domain-specific methodological process that involves the creation, transformation, and selection of input variables from structured tabular datasets to improve the performance and interpretability of machine learning models.
15
Publications
484
Citations
55
Authors
24
Institutions
Publications per year
2017–2026
11
Publications
15
Challenges and opportunities of generative models on tabular data
Alex X. Wang, Stefanka Chukova, Colin R Simpson et al. · Applied Soft Computing · 2024 · 57 citations · Full text
A Catalog of Public Glaucoma Datasets for Machine Learning Applications
Riley Kiefer, Muhammad Rizwan Abid, Jessica Steen et al. · 2023 · 26 citations
1–5 of 15
Authors
55
- EK
1
h-index
- SK
1
h-index
- JS
1
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- SC
1
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- BS
1
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1–5 of 55
Institutions
24
University of Edinburgh
Edinburgh, United Kingdom
2 h-index
1 publications
57 citations
Stanford University
Stanford, United States
2 h-index
6 publications
99 citations
University of Florida
Gainesville, United States
1 h-index
1 publications
10 citations
K J Somaiya Medical College
Mumbai, India
1 h-index
5 publications
105 citations
University of Chicago
Chicago, United States
1 h-index
1 publications
15 citations
University of Notre Dame
Notre Dame, United States
1 h-index
1 publications
35 citations
Mayo Clinic in Arizona
Scottsdale, United States
1 h-index
4 publications
88 citations
University of Maryland, College Park
College Park, United States
1 h-index
2 publications
196 citations
1–8 of 24
Venues
- Proceedings of the VLDB Endowment
PVLDB · 2 publications · 115 citations
2
h-index
- Engineering Applications of Artificial Intelligence
1 publications · 22 citations
1
h-index
- Decision Support Systems
1 publications · 96 citations
1
h-index
- The Journal of Arthroplasty
1 publications · 22 citations
1
h-index
- Applied Soft Computing
1 publications · 57 citations
1
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1–5