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).This function offers a parellel computing.
Data of class:
Number of cores to use (by default:
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.
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. Note that it is not recommanded to
use this function with small data, it takes more time than using the
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.
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N <- 800 dat<-SimData(N) Results<- UfsCov_par(dat,ncores=2) cou<-colnames(dat) nom<-cou[Results[]] par(mfrow=c(1,1), mar=c(5,5,2,2)) names(Results[])<-cou[Results[]] plot(Results[] ,pch=16,cex=1,col="blue", axes = FALSE, xlab = "Added Features", ylab = "Coverage measure") lines(Results[] ,cex=2,col="blue") grid(lwd=1.5,col="gray" ) box() axis(2) axis(1,1:length(nom),nom) which.min(Results[]) ## Not run: N<-5000 dat<-SimData(N) ## Little comparison: system.time(Uf<-UfsCov(dat)) system.time(Uf.p<-UfsCov_par(dat, ncores = 4)) ## End(Not run)
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