| Title: | Sequential Forward Floating Selection using Jeffries-Matusita Distance |
|---|---|
| Description: | Feature selection using Sequential Forward Floating feature Selection and Jeffries-Matusita distance. It returns a suboptimal set of features to use for image classification. Reference: 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, <DOI:10.1109/TGRS.2012.2216272>. |
| Authors: | Michele Dalponte and Hans Ole Oerka |
| Maintainer: | Michele Dalponte <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2 |
| Built: | 2025-01-30 04:06:27 UTC |
| Source: | https://github.com/cran/varSel |
Bhattacharyya distance.
BHATdist(g, X)BHATdist(g, X)
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) |
A list containing a matrix of the class combinations and a vector of the Bhattacharyya distances of all the class combinations.
Michele Dalponte and Hans Ole Oerka
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.
A dataset containing 3230 samples with 65 hyperspectral bands and 8 classes.
data(dat)data(dat)
A data frame with 3230 rows and 66 variables
B1...B65 Hyperspectral bands.
SP. Classes.
Jeffries-Matusita distance.
JMdist(g, X)JMdist(g, X)
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) |
A list containing a matrix of the class combinations and a vector of the JM distances of all the class combinations.
Michele Dalponte and Hans Ole Oerka
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.
Feature selection using the Sequential Forward Floating Selection search strategy and the Jeffries-Matusita distance.
varSelSFFS(g, X, strategy = "mean", n = ncol(X))varSelSFFS(g, X, strategy = "mean", n = ncol(X))
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. |
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).
Michele Dalponte and Hans Ole Oerka
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.
## Not run: data(dat) se<-varSelSFFS(g=dat$SP,X=dat[,c(1:65)],strategy="mean",n=4) summary(se) ## End(Not run)## Not run: data(dat) se<-varSelSFFS(g=dat$SP,X=dat[,c(1:65)],strategy="mean",n=4) summary(se) ## End(Not run)