selectfeatures: Feature selection for classification

In fdm2id: Data Mining and R Programming for Beginners

Feature selection for classification

Description

Select a subset of features for a classification task.

Usage

selectfeatures(
  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("mrmr", "cfs", "fstat",
    "inertiaratio", "wrapper"),
  wrapmethod = NULL,
  keep = 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.
keep	If true, the dataset is kept in the returned result.
...	Other parameters.

See Also

FEATURESELECTION, selection-class

Examples

## Not run: 
require (datasets)
data (iris)
selectfeatures (iris [, -5], iris [, 5], algorithm = "forward", multieval = "fstat")
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking", uninb = 2)
selectfeatures (iris [, -5], iris [, 5], algorithm = "ranking",
                multieval = "wrapper", wrapmethod = LDA)

## End(Not run)

fdm2id documentation built on July 9, 2023, 6:05 p.m.