FeaLect (version 1.20)

FeaLect-package: FeaLect

Description

Suppose you have a feature matrix with 200 features and only 20 samples and your goal is to build a classifier. You can run the FeaLect() function to compute the scores for your features. Only the relatively high score features (say the top 20) are recommended for further analysis. In this way, one can prevent overfitting by reducing the number of features significantly.

Details

The DESCRIPTION file: FeaLect FeaLect

References

Zare, Habil, et al. "Scoring relevancy of features based on combinatorial analysis of Lasso with application to lymphoma diagnosis." BMC genomics. Vol. 14. No. 1. BioMed Central, 2013.

See Also

FeaLect, train.doctor, doctor.validate, random.subset, compute.balanced,compute.logistic.score, ignore.redundant, input.check.FeaLect, lars-package, and SparseLearner-package

Examples

# NOT RUN {
library(FeaLect)
data(mcl_sll)
F <- as.matrix(mcl_sll[ ,-1])	# The Feature matrix
L <- as.numeric(mcl_sll[ ,1])	# The labels
names(L) <- rownames(F)
message(dim(F)[1], " samples and ",dim(F)[2], " features.")

## For this data, total.num.of.models is suggested to be at least 100.
FeaLect.result.1 <-FeaLect(F=F,L=L,maximum.features.num=10,total.num.of.models=20,talk=TRUE)
# }