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

Michel Jose Anzanello and Flavio Sanson Fogliatto

European Journal of Industrial Engineering, 2014, vol. 8, issue 5, 619-645

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. (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.inderscience.com/link.php?id=65731 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:eujine:v:8:y:2014:i:5:p:619-645

Access Statistics for this article

More articles in European Journal of Industrial Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().