run_rafs_with_fs_results {RAFS}

Robust Aggregative Feature Selection (RAFS) from feature selection results

Description

This is a secondary function, useful when experimenting with different feature selection filters and rankings. The output is exactly the same as from run_rafs.

Usage

run_rafs_with_fs_results(
  data,
  decision,
  fs_results,
  dist_funs = default_dist_funs,
  hclust_methods = default_hclust_methods
)

Arguments

data

input data where columns are variables and rows are observations (all numeric)

decision

decision variable as a binary sequence of length equal to number of observations

fs_results

output from compute_fs_results computed for the same data and decision

dist_funs

a list of feature dissimilarity functions computed over the relevant portion of the training dataset (see the example default_dist_funs to learn more)

hclust_methods

a vector of hclust methods to use

Value

A nested list with hclust results. The first level is per the cross validation run. The second level is per the feature dissimilarity function. The third (and last) level is per the hclust method.

Examples

library(MDFS)
mdfs_omp_set_num_threads(1)  # only to pass CRAN checks
data(madelon)
fs_results <- compute_fs_results(madelon$data, madelon$decision, 2, c(12345))
run_rafs_with_fs_results(madelon$data, madelon$decision, fs_results)