
As a supervised feature selection method, WDFS searches over all pairs of between-class and within-class scatters and chooses the highest-scoring features.
do.wdfs(
X,
label,
ndim = 2,
preprocess = c("null", "center", "scale", "cscale", "decorrelate", "whiten")
)
an
a length-
an integer-valued target dimension.
an additional option for preprocessing the data.
Default is "null". See also aux.preprocess
for more details.
a named list containing
an
a length-
a list containing information for out-of-sample prediction.
a
liao_worstcase_2019Rdimtools
# NOT RUN {
## use iris data
## it is known that feature 3 and 4 are more important.
data(iris)
set.seed(100)
subid = sample(1:150,50)
iris.dat = as.matrix(iris[subid,1:4])
iris.lab = as.factor(iris[subid,5])
## compare with other algorithms
out1 = do.lda(iris.dat, iris.lab)
out2 = do.fscore(iris.dat, iris.lab)
out3 = do.wdfs(iris.dat, iris.lab)
## visualize
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,3))
plot(out1$Y, pch=19, col=iris.lab, main="LDA")
plot(out2$Y, pch=19, col=iris.lab, main="FSCORE")
plot(out3$Y, pch=19, col=iris.lab, main="WDFS")
par(opar)
# }
# NOT RUN {
# }
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