CHAVENT, Marie; GENUER, Robin; SARACCO, Jerome

doi:10.1080/03610918.2018.1563145

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CHAVENT, Marie
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]

GENUER, Robin
Statistics In System biology and Translational Medicine [SISTM]

SARACCO, Jerome
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]

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Communications in Statistics - Simulation and Computation. 2021-01-11, vol. 50, n° 2, p. 426-445

Taylor & Francis

Résumé en anglais

Standard approaches to tackle high-dimensional supervised classification often include variable selection and dimension reduction. The proposed methodology combines clustering of variables and feature selection. Hierarchical clustering of variables allows to built groups of correlated variables and summarizes each group by a synthetic variable. Originality is that groups of variables are unknown a priori. Moreover clustering approach deals with both numerical and categorical variables. Among all the possible partitions, the most relevant synthetic variables are selected with a procedure using random forests. Numerical performances are illustrated on simulated and real datasets. Selection of groups of variables provides easier interpretation of results.< Réduire

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Clustering of variables

Random forests

Supervised classification

Variable selection

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Combining clustering of variables and feature selection using random forests

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