Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification

Abstract

Key words: variable selection, support vector machine, recursive feature elimination, fault diagnosis

摘要： Key variable identification for classifications is related to many trouble-shooting problems in process indus-tries. Recursive feature elimination based on support vector machine (SVM-RFE) has been proposed recently in applica-tion for feature selection in cancer diagnosis. In this paper, SVM-RFE is used to the key variable selection in fault diag-nosis, and an accelerated SVM-RFE procedure based on heuristic criterion is proposed. The data from Tennessee East-man 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.

关键词: variable selection;support vector machine;recursive feature elimination;fault diagnosis

MAOYong(毛勇),PIDaoying(皮道映),LIUYuming(刘育明)andSUNYouxian(孙优贤). Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification[J]. .

MAOYong(毛勇),PIDaoying(皮道映),LIUYuming(刘育明)andSUNYouxian(孙优贤). Accelerated Recursive Feature Elimination Based on Support Vector Machine for Key Variable Identification[J]. CIESC Journal.

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