Permutation Feature Importance
Permutation Feature Importance is a model-agnostic interpretability metric that quantifies the relevance of individual features by measuring the degradation in predictive performance when the values of each feature are randomly shuffled, thereby breaking its association with the outcome variable.
111
Publications
7.7K
Citations
562
Authors
257
Institutions
Publications per year
2017–2026
106
Parents
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Permutation importance: a corrected feature importance measure
André Altmann, Laura Toloşi, Oliver Sander et al. · Bioinformatics · 2010 · 2.7K citations · Full text
An explainable deep learning-enabled intrusion detection framework in IoT networks
Marwa Keshk, Nickolaos Koroniotis, Nam Pham et al. · Information Sciences · 2023 · 214 citations · Full text
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Ludwig-Maximilians-Universität München
Munich, Germany
7 h-index
18 publications
1K citations
Georgia Institute of Technology
Atlanta, United States
3 h-index
15 publications
309 citations
University of Michigan
Ann Arbor, United States
3 h-index
7 publications
122 citations
Harvard University
Cambridge, United States
3 h-index
3 publications
88 citations

Washington University in St. Louis
St Louis, United States
3 h-index
12 publications
556 citations

Chinese Academy of Medical Sciences & Peking Union Medical College
Beijing, China
2 h-index
18 publications
594 citations
DHC Software (China)
Beijing, China
2 h-index
6 publications
198 citations
Fudan University
Shanghai, China
2 h-index
9 publications
883 citations
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5 publications · 163 citations
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