Automatic Feature Construction
Automatic Feature Construction is a methodological approach in machine learning that systematically generates higher-order polynomial features from existing variables through automated transformation, thereby expanding the feature space without manual intervention.
217
Publications
13.9K
Citations
746
Authors
343
Institutions
Publications per year
2017–2026
114
Variants
Automated Feature Construction
Parents
217
View-based and modular eigenspaces for face recognition
A Scalable Hierarchical Distributed Language Model
Andriy Mnih, Geoffrey E. Hinton · 2008 · 852 citations
Optimizing deep learning hyper-parameters through an evolutionary algorithm
Steven R. Young, Derek Rose, Thomas P. Karnowski et al. · 2015 · 425 citations
Residual and plain convolutional neural networks for 3D brain MRI classification
Sergey Korolev, Amir Safiullin, Mikhail Belyaev et al. · 2017 · 422 citations
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343

Victoria University of Wellington
Wellington, New Zealand
8 h-index
28 publications
1.6K citations
University of Illinois Urbana-Champaign
Urbana, United States
6 h-index
15 publications
844 citations
IBM (United States)
Armonk, United States
3 h-index
9 publications
726 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
8 publications
311 citations
Technical University of Munich
Munich, Germany
3 h-index
6 publications
559 citations
Stanford University
Stanford, United States
3 h-index
5 publications
74 citations
Queen Mary University of London
London, United Kingdom
3 h-index
7 publications
1.2K citations
University of Chinese Academy of Sciences
Beijing, China
3 h-index
7 publications
245 citations
1–8 of 343
Repository · 7 publications · 175 citations
7
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7 publications · 309 citations
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TPAMI · 4 publications · 179 citations
4
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3 publications · 214 citations
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3 publications · 87 citations
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