varSelSFFS: Sequential Forward Floating Selection using Jeffries-Matusita...

In varSel: 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)

varSel documentation built on Jan. 7, 2021, 5:11 p.m.