Feature selection for classification
Select a subset of features for a classification task.
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,
...
)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. |
keep |
If true, the dataset is kept in the returned result. |
... |
Other parameters. |
## 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.