Automated Feature Engineering
Automated Feature Engineering is an academic method within the domain of feature construction that systematically generates domain-specific features from raw data through algorithmic means, distinguishing itself from manual feature design by enabling scalable, reproducible, and context-adaptive feature discovery without relying on extensive human intervention.
210
Publications
13.6K
Citations
776
Authors
343
Institutions
Publications per year
2017–2026
126
210
Automatically learning semantic features for defect prediction
Song Wang, Taiyue Liu, Lin Tan · 2016 · 692 citations
1–5 of 210
776
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6
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6
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5
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IBM Research - Thomas J. Watson Research Center, United States
4 publications · 654 citations
4
h-index
1–5 of 776
343

Universidad de Sevilla
Seville, Spain
6 h-index
18 publications
1.5K citations

Johannes Kepler University of Linz
Linz, Austria
4 h-index
16 publications
439 citations
Massachusetts Institute of Technology
Cambridge, United States
4 h-index
12 publications
560 citations
Stanford University
Stanford, United States
4 h-index
11 publications
326 citations
IBM (United States)
Armonk, United States
3 h-index
10 publications
754 citations

Centre National de la Recherche Scientifique
Paris, France
3 h-index
8 publications
444 citations
University of Illinois Urbana-Champaign
Urbana, United States
3 h-index
7 publications
1.4K citations
KU Leuven
Leuven, Belgium
3 h-index
10 publications
1.3K citations
1–8 of 343
10 publications · 767 citations
10
h-index
Repository · 8 publications · 536 citations
8
h-index
TSE · 3 publications · 173 citations
3
h-index
3 publications · 552 citations
3
h-index
2 publications · 101 citations
2
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1–5