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

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

MAOYong(毛勇),PIDaoying(皮道映),LIUYuming(刘育明)andSUNYouxian(孙优贤)

National Laboratory of Industrial Control Technology, Institute of Modern Control Engineering, Zhejiang University, Hangzhou 310027, China National Laboratory of Industrial Control Technology, Institute of System Control Engineering, Zhejiang University, Hangzhou 310027, China

收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2006-02-28 发布日期:2006-02-28

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

Received:1900-01-01 Revised:1900-01-01 Online:2006-02-28 Published:2006-02-28

摘要/Abstract

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

Key words: 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]. CIESC Journal.

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]. .

参考文献

1    Liang, J., Qian, J.X., “Multivariate statistical process monitoring and control: Recent developments and appli-cations to chemical industry”, Chinese J. Chem. Eng., 11(2), 191—203 (2003).
2    Venkatasubramanian, V., Rengaswamy, R., Kavuri, S.N., Yin, K.W., “A review of process fault detection and di-agnosis Part III: Process history based methods”, Comp. Chem. Eng., 27, 327—346 (2003).
3    Chu, Y.H., Qin, S. J., Han, C., “Fault detection and op-eration identification based on pattern classification with variable selection”, Ind. Eng. Chem. Res., 43, 1701—1710 (2004).
4    Chen, F.Z., Wang, X.Z., “Discovery of operational spaces from process data for production of multiple grades of products”, Ind. Eng. Chem. Res., 39, 2378—2383 (2000).
5    Miller, P., Swanson, R.E., “Contribution plots: A miss-ing link in multivariate quality control”, Appl. Math.  Comp. Sci., 8, 775—792 (1998).
6    Chiang, L.H., Pell, R.J., “Genetic algorithms combined sswith discriminant analysis for key variable identifica-tion”, J. Process Control, 14, 143—155 (2004).
7    Guyon, I., Weston, J., Barnhill, S., Vapnik, V., “Gene selection for cancer classification using support vector machines”, Mach. Learn., 46, 389—422 (2002).
8    Mao, Y., Zhou, X., Pi, D., Sun, Y., Wong, S.T.C., “Multi-Class cancer classification by using fuzzy support vector machine and binary decision tree with gene selec-tion”, J. Biomed. Biotechnol., 2, 160—171 (2005).
9    Vapnik, V.N., The Nature of Statistical Learning Theory, 2nd ed., Springer, New York (2000).
10    Mao, Y., Zhou, X., Pi, D., Sun, Y., Wong, S.T.C., “Pa-rameters selection in gene selection using Gaussian Ker-nel support vector machines by genetic algorithm”, J. Zhejiang Univ. S, 6B, 961—973 (2005).
11    Lyman, P.R., Georgakis, C., “Plant-wide control of the Tennessee Eastman problem”, Comp. Chem. Engr., 19, 321—331 (1995).
12    Downs, J.J., Vogel, E.F., “A plant-wide indus-trial-process control problem”, Comp. Chem. Engr., 17, 245—255 (1993).
13    Chiang, L. H., Russell, E.L., Braatz, R.D., Data-driven Methods for Fault Detection and Diagnosis in Chemical Process, Springer, London (2000).

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

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

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