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Table 1 Method overview

From: EFS: an ensemble feature selection tool implemented as R-package and web-application

Command Parameters Information
ensemble_fs data object of class data.frame
  classnumber index of variable for binary classification
  NA_threshold threshold for deletion of features with a greater proportion of NAs
  cor_threshold correlation threshold within features
  runs amount of runs for randomForest and cforest
  selection selection of feature selection methods to be conducted
barplot_fs name character string giving the name of the file
  efs_table table object of class matrix retrieved from ensemble_fs
efs_eval data object of class data.frame
  efs_table table object of class matrix retrieved from ensemble_fs
  file_name character string, name which is used for the two possible PDF files.
  classnumber index of variable for binary classification
  NA_threshold threshold for deletion of features with a greater proportion of NAs
  logreg logical value indicating whether to conduct an evaluation via logistic regression or not
  permutation logical value indicating whether to conduct a permutation of the class variable or not
  p_num number of permutations; default set to a 100
  variances logical value indicating whether to calculate the variances of importances retrieved
   from bootstrapping or not
  jaccard logical value indicating whether to calculate the Jaccard-index or not
  bs_num number of bootstrap permutations of the importances
  bs_percentage proportion of randomly selected samples for bootstrapping
  1. The R-package EFS provides three functions