Feature Selection For Regression
Feature Selection for Regression is a methodological framework within feature selection that specifically identifies and retains predictor variables most relevant to predicting continuous outcome variables in regression analysis, distinguishing it from feature selection tasks targeting classification or unsupervised learning.
239
Publications
26.4K
Citations
729
Authors
347
Institutions
Publications per year
2017–2026
128
Parents
239
An Introduction to Variable and Feature Selection.
2003 · 6.6K citations
Fast Binary Feature Selection with Conditional Mutual Information
François Fleuret · 2004 · 836 citations
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347

Northwestern Polytechnical University
Xi'an, China
6 h-index
18 publications
756 citations
Yale University
New Haven, United States
5 h-index
13 publications
777 citations
National University of Singapore
Singapore, Singapore
5 h-index
10 publications
389 citations
University of Pennsylvania
Philadelphia, United States
5 h-index
11 publications
790 citations

Guangxi Normal University
Guilin, China
4 h-index
18 publications
494 citations
Korea University
Seoul, South Korea
4 h-index
10 publications
1.1K citations

Deakin University
Burwood, Australia
4 h-index
10 publications
626 citations
University of Mosul
Mosul, Iraq
3 h-index
8 publications
293 citations
1–8 of 347
11 publications · 610 citations
11
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7 publications · 1.3K citations
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7 publications · 334 citations
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5 publications · 621 citations
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5 publications · 323 citations
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