UfsCov: UfsCov algorithm for unsupervised feature selection

In mlaib/SFtools: Space Filling Based Tools for Data Mining

Description

Applies the UfsCov algorithm based on the space filling concept, by using a sequatial forward search (SFS).

Usage

UfsCov(data)

Arguments

data	Data of class: matrix or data.frame.

Details

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.

Value

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.

Note

The algorithm does not deal with missing values and constant features. Please make sure to remove them.

Author(s)

Mohamed Laib [e-mail]

References

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.

Examples

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)

mlaib/SFtools documentation built on Feb. 1, 2021, 6:11 p.m.