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") c("fisher", "fstat", "relief", "inertiaratio")
    else NULL,
  uninb = NULL,
  unithreshold = NULL,
  multieval = if (algorithm[1] == "ranking") NULL else c("cfs", "fstat", "inertiaratio",
    "wrapper"),
  wrapmethod = NULL,
  mainmethod = wrapmethod,
  tune = FALSE,
  ...
)

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. If a wrapper evaluation is used, the same classification method should be used.
tune	If true, the function returns paramters instead of a classification model.
...	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 July 9, 2023, 6:05 p.m.