Premium
Input variable selection for an inferential predictor using the retrospective Taguchi method
Author(s) -
Rahman M. Musfiqur,
Imtiaz Syed Ahmad,
Hawboldt Kelly,
Zhang Yan
Publication year - 2015
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.22222
Subject(s) - taguchi methods , support vector machine , variable (mathematics) , feature selection , process (computing) , computer science , selection (genetic algorithm) , regression analysis , machine learning , artificial intelligence , data mining , mathematics , mathematical analysis , operating system
A systematic method based on Taguchi's experimental design approach is proposed for selecting input variables for an inferential predictor. Several implementation difficulties arising from dynamic variation and correlation among process variables are addressed. The predictor is developed using support vector regression (SVR) in order to capture the nonlinearity in the process. The prediction performance of the proposed Taguchi‐SVR is compared with the existing variable importance in projection (VIP)'SVR method. The industrial case study clearly indicates that the proposed methodology can be a valuable tool for process variable selection and it can improve the prediction performance of the inferential predictor.