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A review of recent variable selection methods in industrial and chemometrics applications

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  • Michel Jose Anzanello
  • Flavio Sanson Fogliatto

Abstract

The massive amount of data collected from industrial processes has challenged researchers and practitioners, turning variable selection into a research topic of interest both in academia and in industry. The use of redundant, irrelevant, and noisy variables tends to compromise the performance of many statistical tools, leading to unreliable inferences and costly data collection. In this paper, we present a literature review on recent variable selection methods and applications in manufacturing and in the chemometrics field. These methods are deployed into two major categories: variable selection for prediction of continuous response variables and for prediction of a categorical variable (also referred to as classification). Future research directions are also outlined. [Received 28 May 2012; Revised 19 December 2012; Revised 22 April 2013; Accepted 25 May 2013]

Suggested Citation

  • Michel Jose Anzanello & Flavio Sanson Fogliatto, 2014. "A review of recent variable selection methods in industrial and chemometrics applications," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 8(5), pages 619-645.
    • Handle: RePEc:ids:eujine:v:8:y:2014:i:5:p:619-645
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    Cited by:

    1. Laura Vicente-Gonzalez & Jose Luis Vicente-Villardon, 2022. "Partial Least Squares Regression for Binary Responses and Its Associated Biplot Representation," Mathematics, MDPI, vol. 10(15), pages 1-23, July.
    2. Orea, Luis & Zofío, José L., 2017. "A primer on the theory and practice of efficiency and productivity analysis," Efficiency Series Papers 2017/05, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).

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