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

Authors: Michel Jose Anzanello; Flavio Sanson Fogliatto

Addresses: Federal University of Rio Grande do Sul (UFRGS), Av. Osvaldo Aranha, 99, Porto Alegre – CEP 90.035-190, Rio Grande do Sul, Brazil ' Federal University of Rio Grande do Sul (UFRGS), Av. Osvaldo Aranha, 99, Porto Alegre – CEP 90.035-190, Rio Grande do Sul, Brazil

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]

Keywords: variable selection; data mining; chemometrics applications; partial least squares; PLS; manufacturing applications; literature review; continuous response variables; categorical variables; classification.

DOI: 10.1504/EJIE.2014.065731

European Journal of Industrial Engineering, 2014 Vol.8 No.5, pp.619 - 645

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 12 Nov 2014 *

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