Selection of relevant variables for industrial process modeling by combining experimental data sensitivity and human knowledge

Authors:
Xiaoguang Deng;Xianyi Zeng;Philippe Vroman;Ludovic Koehl
Affiliations:
Univ Lille Nord de France, F-59000, Lille, France and ENSAIT, GEMTEX, F-59100, Roubaix, France;Univ Lille Nord de France, F-59000, Lille, France and ENSAIT, GEMTEX, F-59100, Roubaix, France;Univ Lille Nord de France, F-59000, Lille, France and ENSAIT, GEMTEX, F-59100, Roubaix, France and Centre européen du non-tissé, F-59200, Tourcoing, France;Univ Lille Nord de France, F-59000, Lille, France and ENSAIT, GEMTEX, F-59100, Roubaix, France
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

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Abstract

Selection of relevant variables from a high dimensional process operation setting space is a problem frequently encountered in industrial process modeling. This paper presents two global relevancy criteria, which permit to formalize and combine the sensitivity of experimental data and the conformity of human knowledge using a liner and a fuzzy model, respectively. The performances of these relevancy criteria and some well-known selection methods are compared through artificial and real datasets. The result validates the outperformance of fuzzy global relevancy criterion, especially when the number of learning data is small and noisy.