Classification with Feature selection
Apply a classification method after a subset of features has been selected.
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,
...
)train |
The training set (description), as a |
labels |
Class labels of the training set ( |
algorithm |
The feature selection algorithm. |
unieval |
The (univariate) evaluation criterion. |
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. |
## Not run: require (datasets) data (iris) FEATURESELECTION (iris [, -5], iris [, 5], uninb = 2, mainmethod = LDA) ## End(Not run)
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