FeatureSelection: Feature selection for conditional random forests.

In nicolas-robette/moreparty: A Toolbox for Conditional Inference Trees and Random Forests

Feature selection for conditional random forests.

Description

Performs feature selection for a conditional random forest model. Four approaches are available : non-recursive feature elimination (NRFE), recursive feature elimination (RFE), permutation test approach with permuted response (Altmann et al, 2010), permutation test approach with permuted predictors (Hapfelmeier et Ulm, 2013).

Usage

FeatureSelection(Y, X, method = 'NRFE', ntree = 1000, measure = NULL,
                 nperm = 30, alpha = 0.05, distrib = 'approx',
                 parallel = FALSE, ...)

Arguments

Y	response vector. Must be of class factor or numeric
X	matrix or data frame containing the predictors
method	method for feature selection. Should be 'NRFE' (non-recursive feature elimination, default), 'RFE' (recursive feature elimination), 'ALT' (permutation of response) or 'HAPF' (permutation of predictors)
ntree	number of trees contained in a forest
measure	the name of the measure of the measures package that should be used for error and variable importance calculations.
nperm	number of permutations. Only for 'ALT' and 'HAPF' methods.
alpha	alpha level for permutation tests. Only for 'ALT' and 'HAPF' methods.
distrib	the null distribution of the variable importance can be approximated by its asymptotic distribution ("asympt") or via Monte Carlo resampling ("approx", default). Only for 'ALT' and 'HAPF' methods.
parallel	Logical indicating whether or not to run fastvarImp in parallel using a backend provided by the foreach package. Default is FALSE.
...	Further arguments (like positive or negative class) that are needed by the measure.

Details

To be developed soon !

Value

A list with the following elements :

selection.0se	selected variables with the 0 standard error rule
forest.0se	forest corresponding the variables selected with the 0 standard error rule
oob.error.0se	OOB error of the forest with 0 standard error rule
selection.1se	selected variables with the 1 standard error rule
forest.1se	forest corresponding the variables selected with the 1 standard error rule
oob.error.1se	OOB error of the forest with 1 standard error rule

Note

The code is adapted from Hapfelmeier & Ulm (2013).

Only works for regression and binary classification.

Author(s)

Nicolas Robette

References

B. Gregorutti, B. Michel, and P. Saint Pierre. "Correlation and variable importance in random forests". arXiv:1310.5726, 2017.

A. Hapfelmeier and K. Ulm. "A new variable selection approach using random forests". Computational Statistics and Data Analysis, 60:50–69, 2013.

A. Altmann, L. Toloşi, O. Sander et T. Lengauer. "Permutation importance: a corrected feature importance measure". Bioinformatics, 26(10):1340-1347, 2010.

Examples

  data(iris)
  iris2 = iris
  iris2$Species = factor(iris$Species == "versicolor")
  featsel <- FeatureSelection(iris2$Species, iris2[,1:4], measure='ACC', ntree=200)
  featsel$selection.0se
  featsel$selection.1se

nicolas-robette/moreparty documentation built on April 10, 2024, 2:24 p.m.