中国化学工程学报（英文版）
中國化學工程學報（英文版）
중국화학공정학보（영문판）
CHINESE JOURNAL OF CHEMICAL ENGINEERING
2006年
1期
65-72
,共8页
毛勇%皮道映%刘育明%孙优贤
毛勇%皮道映%劉育明%孫優賢
모용%피도영%류육명%손우현
variable selection%support vector machine%recursive feature elimination%fault diagnosis
Key variable identification for classifications is related to many trouble-shooting problems in process industries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in application for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diagnosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee Eastman process (TEP) simulator is used to evaluate the effectiveness of the key variable selection using accelerated SVM-RFE (A-SVM-RFE). A-SVM-RFE integrates computational rate and algorithm effectiveness into a consistent framework. It not only can correctly identify the key variables, but also has very good computational rate. In comparison with contribution charts combined with principal component aralysis (PCA) and other two SVM-RFE algorithms,A-SVM-RFE performs better. It is more fitting for industrial application.
