| varSelSFFS {varSel} | R Documentation |
Sequential Forward Floating Selection using Jeffries-Matusita Distance
Description
Feature selection using the Sequential Forward Floating Selection search strategy and the Jeffries-Matusita distance.
Usage
varSelSFFS(g, X, strategy = "mean", n = ncol(X))
Arguments
g |
A column vector of the lables. length(g) is equal to nrow(X). |
X |
A dataframe of the features. ncol(X) is equal to the total number of features, and nrow(X) is equal to the number of avaialble training samples. nrow(X) is equal to length(g) |
strategy |
string indicating the multiclass strategy to adopt: 'minimum' or 'mean'. |
n |
integer indicating the number of features to select. The algorithm will stop at n+1 features selected. |
Value
A list containing a vector of the JM distances on the individual bands, a matrix with the set of features selected, and a vector containing the distances for each feature set from 1 to N-1, where N is equal to ncol(X).
Author(s)
Michele Dalponte and Hans Ole Oerka
References
Dalponte, M., Oerka, H.O., Gobakken, T., Gianelle, D. & Naesset, E. (2013). Tree Species Classification in Boreal Forests With Hyperspectral Data. IEEE Transactions on Geoscience and Remote Sensing, 51, 2632-2645.
Examples
## Not run:
data(dat)
se<-varSelSFFS(g=dat$SP,X=dat[,c(1:65)],strategy="mean",n=4)
summary(se)
## End(Not run)