Provides a genetic algorithm for finding variable subsets in high dimensional data with high prediction performance. The genetic algorithm can use ordinary least squares (OLS) regression models or partial least squares (PLS) regression models to evaluate the prediction power of variable subsets. By supporting different cross-validation schemes, the user can fine-tune the tradeoff between speed and quality of the solution.
This R package implements a genetic algorithm (GA) for variable selection as described in Kepplinger, D., Filzmoser, P., and Varmuza, K. (2017). Variable selection with genetic algorithms using repeated cross-validation of PLS regression models as fitness measure. https://arxiv.org/abs/1711.06695.
To install the latest release from CRAN, run the following R code in the R console:
install.packages('gaselect')
The most recent stable version as well as the developing version might not yet be available on CRAN. These can be directly installed from github using the devtools package:
# Install the most recent stable version:
install_github('dakep/gaselect')
# Install the (unstable) develop version:
install_github('dakep/gaselect', ref = 'develop')