FEATURESELECTION: Classification with Feature selection

In fdm2id: Data Mining and R Programming for Beginners

Classification with Feature selection

Description

Apply a classification method after a subset of features has been selected.

Usage

FEATURESELECTION(
  train,
  labels,
  algorithm = c("ranking", "forward", "backward", "exhaustive"),
  unieval = if (algorithm[1] == "ranking") fseval.univariate() else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = fseval.multivariate(),
  wrapmethod = NULL,
  mainmethod = wrapmethod,
  tune = FALSE,
  methodparameters = NULL,
  graph = FALSE,
  seed = NULL,
  ...
)

Arguments

train	The training set (description), as a data.frame.
labels	Class labels of the training set (vector or factor).
algorithm	The feature selection algorithm.
unieval	The (univariate) evaluation criterion. uninb, unithreshold or multieval must be specified.
uninb	The number of selected feature (univariate evaluation).
unithreshold	The threshold for selecting feature (univariate evaluation).
multieval	The (multivariate) evaluation criterion.
wrapmethod	The classification method used for the wrapper evaluation.
mainmethod	The final method used for data classification (required: either mainmethod or wrapmethod must be a valid classification/regression function, e.g. LDA, NB, ...). If a wrapper evaluation is used, the same classification method should be used.
tune	If true, the function returns parameters instead of a classification model.
methodparameters	Pre-tuned parameters, as returned by the same method called with tune = TRUE. performance obtains them once and passes them back when fitting, so that the tuning is not redone on every split. A method with nothing to tune returns an empty object, which leaves its defaults untouched.
graph	Whether the method draws the graphic that goes with its tuning (the cross-validation curve, typically). Methods that have no such graphic accept the argument and ignore it.
seed	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
...	Other parameters.

See Also

selectfeatures, predict.selection, selection-class

Examples

## Not run: 
require (datasets)
data (iris)
FEATURESELECTION (iris [, -5], iris [, 5], uninb = 2, mainmethod = LDA)

## End(Not run)

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.