UfsCov_par: UfsCov algorithm for unsupervised feature selection

In 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).This function offers a parellel computing.

Usage

UfsCov_par(data, ncores=2)

Arguments

data	Data of class: matrix or data.frame.
ncores	Number of cores to use (by default: ncores=2).

Details

Since the algorithm is based on pairwise distances, and according to the computing power of your machine, large number of data points needs more memory. See UfsCov_ff for memory efficient storage of large data on disk and fast access (by using the ff and the ffbase packages).

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. Note that it is not recommanded to use this function with small data, it takes more time than using the standard UfsCov function.

Author(s)

Mohamed Laib [e-mail]

References

M. Laib and M. Kanevski (2017). Unsupervised Feature Selection Based on Space Filling Concept, arXiv:1706.08894.

See Also

UfsCov, UfsCov_ff

Examples

N <- 800
dat<-Infinity(N)
Results<- UfsCov_par(dat,ncores=2)

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]])

## Not run: 

N<-5000
dat<-Infinity(N)

## Little comparison:
system.time(Uf<-UfsCov(dat))
system.time(Uf.p<-UfsCov_par(dat, ncores = 4))


## End(Not run)

SFtools documentation built on May 2, 2019, 11:02 a.m.