FSinR: an exhaustive package for feature selection
This provides a software solution for researchers and practitioners in machine learning who require feature selection capabilities, but it is incremental as it packages existing methods.
The authors tackled the need for a comprehensive feature selection tool in R by developing the FSinR package, which implements various filter and wrapper methods along with search algorithms, and they demonstrated its usage through examples and comparisons with other R packages.
Feature Selection (FS) is a key task in Machine Learning. It consists in selecting a number of relevant variables for the model construction or data analysis. We present the R package, FSinR, which implements a variety of widely known filter and wrapper methods, as well as search algorithms. Thus, the package provides the possibility to perform the feature selection process, which consists in the combination of a guided search on the subsets of features with the filter or wrapper methods that return an evaluation measure of those subsets. In this article, we also present some examples on the usage of the package and a comparison with other packages available in R that contain methods for feature selection.