"A method by which to decide on which features (columns) to keep in the analysis that will be done by the data mining algorithms. One of the first things to be done in a data mining project; this uncovers the most important variables among the set of predictor variables. Many of the predictor variables in a data set may not really be important for making an accurate predictive model, and only dilute/reduce the accuracy score of the model if included." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)
"The process a cybersecurity engineer uses to choose the features in which a given attack may manifest." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)
"Feature selection is the process of selecting important principal variables (features) from some random variables under consideration, usually achieved by selecting a principal variable (feature) as one of the random variables." (Usama A Khan & Josephine M Namayanja, "Reevaluating Factor Models: Feature Extraction of the Factor Zoo", 2021)
"It is used to select appropriate features from the available data for improving efficiency of machine learning algorithms." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)
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