Description Usage Arguments Details Value Note Author(s) References Examples
Applies the UfsCov algorithm based on the space filling concept, by using a sequatial forward search (SFS).
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Data of class: |
Since the algorithm is based on pairwise distances, and according to the computing power of your machine, large number of data points can take much time and needs more memory.
A list of two elements:
CovD
a vector containing the coverage measure of
each step of the SFS.
IdR
a vector containing the added variables during
the selection procedure.
The algorithm does not deal with missing values and constant features. Please make sure to remove them.
Mohamed Laib Mohamed.Laib@unil.ch
M. Laib, M. Kanevski, A novel filter algorithm for unsupervised feature selection based on a space filling measure. Proceedings of the 26rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), pp. 485-490, Bruges (Belgium), 2018.
M. Laib and M. Kanevski, A new algorithm for redundancy minimisation in geo-environmental data, 2019. Computers & Geosciences, 133 104328.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 | Sim_Data<-SimData(n=800)
Results<- UfsCov(Sim_Data)
cou<-colnames(Sim_Data)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])
## Not run:
#### UfsCov on the Butterfly dataset ####
require(IDmining)
N <- 1000
raw_dat <- Butterfly(N)
dat<-raw_dat[,-9]
Results<- UfsCov(dat)
cou<-colnames(dat)
nom<-cou[Results[[2]]]
par(mfrow=c(1,1), mar=c(5,5,2,2))
names(Results[[1]])<-cou[Results[[2]]]
plot(Results[[1]] ,pch=16,cex=1,col="blue", axes = FALSE,
xlab = "Added Features", ylab = "Coverage measure")
lines(Results[[1]] ,cex=2,col="blue")
grid(lwd=1.5,col="gray" )
box()
axis(2)
axis(1,1:length(nom),nom)
which.min(Results[[1]])
## End(Not run)
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