Variable selection by random forests using data with missing values

DOI10.1016/j.csda.2014.06.017zbMath1506.62075OpenAlexW1999795754MaRDI QIDQ1623702

Kurt Ulm, Alexander Hapfelmeier

Publication date: 23 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.06.017

zbMATH Keywords

missing datamultiple imputationvariable selectionrandom forestsvariable importancecomplete case analysis

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

Subgroup identification by recursive segmentation ⋮ Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models ⋮ Efficient permutation testing of variable importance measures by the example of random forests

Uses Software

• R
• party
• GeneSrF
• MICE
• C4.5

Cites Work

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• A new variable selection approach using random forests
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• Multiple Imputation After 18+ Years
• Fully conditional specification in multivariate imputation
• Inference and missing data
• 10.1162/153244303322753616
• Multiple imputation of discrete and continuous data by fully conditional specification
• Random forests

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