论文标题
FSINR：详尽的功能选择包
FSinR: an exhaustive package for feature selection
论文作者
论文摘要
功能选择（FS）是机器学习的关键任务。它包括为模型构建或数据分析选择许多相关变量。我们提出R软件包FSINR，该软件包实现了各种已知的过滤器和包装器方法以及搜索算法。因此，软件包提供了执行特征选择过程的可能性，该过程包括在功能子集对特征子集的指导搜索以及返回这些子集的评估度量的滤镜或包装法的组合。在本文中，我们还提供了一些有关包装使用情况的示例，以及与R中可用的其他软件包的比较，这些示例包含用于功能选择的方法。
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.
