Feature Selection - Software For Feature Selection

Software For Feature Selection

Many standard data analysis software systems are often used for feature selection, such as SciLab, NumPy and the R language. Other software systems are tailored specifically to the feature-selection task:

• Weka – freely available and open-source software in Java.
• Feature Selection Toolbox 3 – freely available and open-source software in C++.
• RapidMiner – freely available and open-source software.
• Orange – freely available and open-source software (module orngFSS).
• TOOLDIAG Pattern recognition toolbox – freely available C toolbox.
• minimum redundancy feature selection tool – freely available C/Matlab codes for selecting minimum redundant features.
• A C# Implementation of greedy forward feature subset selection for various classifiers (e.g., LibLinear, SVM-light).
• MCFS-ID (Monte Carlo Feature Selection and Interdependency Discovery) is a Monte Carlo method-based tool for feature selection. It also allows for the discovery of interdependencies between the relevant features. MCFS-ID is particularly suitable for the analysis of high-dimensional, ill-defined transactional and biological data.
• RRF is an R package for feature selection and can be installed from R. RRF stands for Regularized Random Forest, which is a type of Regularized Trees. By building a regularized random forest, a compact set of non-redundant features can be selected without loss of predictive information. Regularized trees can capture non-linear interactions between variables, and naturally handle different scales, and numerical and categorical variables.