An Introduction to Feature Selection

Max Kuhn and Kjell Johnson
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Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 19 in Applied Predictive Modeling, 2013, pp 487-519 from Springer

Abstract: Abstract Determining which predictors should be included in a model is becoming one of the most critical questions as data are becoming increasingly high-dimensional. The chapter demonstrates the negative effect of extra predictors on a number of models (Section 19.1), as well as discussing typical approaches to supervised feature selection such as wrapper and filter methods (Sections 19.2-19.4). The modeler should also be aware of the danger of selection bias and how to avoid it (Section 19.5). In Section 19.6 we present a case study to illustrate the feature selection methods. In the Computing Section (19.7) we demonstrate how to implement feature selection methodologies in R. Finally, exercises are provided at the end of the chapter to solidify the concepts.

Keywords: Support Vector Machine; Feature Selection; Random Forest; Linear Discriminant Analysis; Filter Method (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/978-1-4614-6849-3_19

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