Explore: Feature Selection

feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Feature selection techniques

Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow

in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. It was originally designed for application

or One-Button Machine combines feature transformations and feature selection on relational data with feature selection techniques. [OneBM] helps data

nonlinear approaches. Linear approaches can be further divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction

Feature Selection Toolbox (FST) is software primarily for feature selection in the machine learning domain, written in C++, developed at the Institute

Berlin: Springer. ISBN 0-387-31073-8. Liu, H., Motoda H. (1998) Feature Selection for Knowledge Discovery and Data Mining., Kluwer Academic Publishers

model selection include feature selection, hyperparameter optimization, and statistical learning theory. In its most basic forms, model selection is one

propagation. Feature selection algorithms attempt to directly prune out redundant or irrelevant features. A general introduction to feature selection which summarizes

that facilitates object recognition while a semi-supervised MPCA feature selection is employed in visualization tasks. Various extension of MPCA: Robust