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

被引:29
作者
Anzanello, Michel Jose [1 ]
Fogliatto, Flavio Sanson [1 ]
机构
[1] Fed Univ Rio Grande Sul UFRGS, BR-90035190 Porto Alegre, RS, Brazil
关键词
variable selection; data mining; chemometrics applications; partial least squares; PLS; manufacturing applications; LEAST-SQUARES REGRESSION; PRINCIPAL COMPONENT ANALYSIS; MULTIVARIATE CALIBRATION; GENETIC ALGORITHM; PLS-REGRESSION; EXPLANATORY VARIABLES; PATTERN-RECOGNITION; SUBSET-SELECTION; CLASSIFICATION; PREDICTION;
D O I
10.1504/EJIE.2014.065731
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页码:619 / 645
页数:27
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