sfs: Sequential Forward Selection
In dprep: Data Pre-Processing and Visualization Functions for Classification

Description Usage Arguments Details Value Author(s) References Examples

Applies the Sequential Forward Selection algorithm for Feature Selection.

1 2	sfs(data, method = c("lda", "knn", "rpart"), kvec = 5, repet = 10)

`data`	Dataset to be used for feature selection
`method`	Classifier to be used, currently only the lda, knn and rpart classifiers are supported
`kvec`	Number of neighbors to use for the knn classification
`repet`	Number of times to repeat the selection.

The best subset of features, T, is initialized as the empty set and at each step the feature that gives the highest correct classification rate along with the features already in T, is added to set. The "best subset" of features is constructed based on the frequency with which each attribute is selected in the number of repetitions given. Due to the time complexity of the algorithm its use is not recommended for datasets with a large number of attributes(say more than 1000).

bestsubset

subset of features that have been determined to be relevant.

Edgar Acuna

Acuna, E , (2003) A comparison of filters and wrappers for feature selection in supervised classification. Proceedings of the Interface 2003 Computing Science and Statistics. Vol 34.

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#---- Sequential forward selection using the knn classifier----
data(iris)
sfs(iris,method="lda",repet=3)

Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE 
3: .onUnload failed in unloadNamespace() for 'rgl', details:
  call: fun(...)
  error: object 'rgl_quit' not found 
The best subset of features is:
[1] 3