Combining clustering of variables and feature selection using random forests

DOI10.1080/03610918.2018.1563145zbMath1489.62179arXiv1608.06740OpenAlexW2899553782MaRDI QIDQ5083993

Jérôme Saracco, Robin Genuer, Marie Chavent

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.06740

zbMATH Keywords

variable selectionrandom forestssupervised classificationclustering of variables

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (1)

Dendrograms, minimum spanning trees and feature selection

Uses Software

• R
• glasso
• PCAmixdata
• mixOmics
• randomForest
• ClustOfVar
• clusfind
• VSURF

Cites Work

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• Orthogonal rotation in PCAMIX
• Simple structure in component analysis techniques for mixtures of qualitative and quantitative variables
• ClustGeo: an R package for hierarchical clustering with spatial constraints
• Principal component analysis.
• Variable Selection for Clustering with Gaussian Mixture Models
• Finding Groups in Data
• Selection bias in gene extraction on the basis of microarray gene-expression data
• Sparse Partial Least Squares Regression for Simultaneous Dimension Reduction and Variable Selection
• 10.1162/153244303322753616
• Regularization and Variable Selection Via the Elastic Net
• Model Selection and Estimation in Regression with Grouped Variables
• Bayesian Variable Selection in Clustering High-Dimensional Data
• Variable Selection for Model-Based Clustering
• Random forests

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