Random forest-based approach for physiological functional variable selection for driver's stress level classification

DOI10.1007/S10260-018-0423-5zbMath1427.62140OpenAlexW2599893746MaRDI QIDQ2324299

Jean-Michel Poggi, Raja Ghozi, Neska El Haouij, Sylvie Sevestre-Ghalila, Mériem Jaïdane

Publication date: 11 September 2019

Published in: Statistical Methods and Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10260-018-0423-5

zbMATH Keywords

waveletsrandom forestsfunctional datarecursive feature eliminationphysiological signalsgrouped variable importance

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to biology and medical sciences; meta analysis (62P10) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Applications of statistics in engineering and industry; control charts (62P30)

Related Items (3)

Supervised classification of curves via a combined use of functional data analysis and tree-based methods ⋮ Random forest-based approach for physiological functional variable selection for driver's stress level classification ⋮ Variable selection and importance in presence of high collinearity: an application to the prediction of lean body mass from multi-frequency bioelectrical impedance

Uses Software

• Unnamed Item
• fda (R)
• GeneSrF
• randomForest
• PhysioToolkit
• RFgroove
• VSURF

Cites Work

• Unnamed Item
• Unnamed Item
• Correlation and variable importance in random forests
• Applied functional data analysis. Methods and case studies
• Grouped variable importance with random forests and application to multiple functional data analysis
• Principal component analysis.
• Random forest-based approach for physiological functional variable selection for driver's stress level classification
• Functional data analysis.
• Nonparametric functional data analysis. Theory and practice.
• A theory for multiresolution signal decomposition: the wavelet representation
• The elements of statistical learning. Data mining, inference, and prediction
• Random forests
• Gene selection for cancer classification using support vector machines

This page was built for publication: Random forest-based approach for physiological functional variable selection for driver's stress level classification