Accelerated recursive feature elimination based on support vector machine for key variable identification

Chinese Journal of Chemical Engineering

Volumn 14, Issue 1, 2006, Pages 65-72

  Mao, Yong ^a   Pi, Daoying ^a   Liu, Yuming ^a   Sun, Youxian ^a  

^a STATE KEY LABORATORY OF INDUSTRIAL CONTROL TECHNOLOGY   (China)

Author keywords

Fault diagnosis; Recursive feature elimination; Support vector machine; Variable selection

Indexed keywords

CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; HEURISTIC METHODS; PRINCIPAL COMPONENT ANALYSIS; RECURSIVE FUNCTIONS; STATISTICAL PROCESS CONTROL;

FAULT DIAGNOSIS; RECURSIVE FEATURE ELIMINATION; SUPPORT VECTOR MACHINE; TENNESSEE EASTMAN PROCESS (TEP); VARIABLE SELECTION;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 33646129443     PISSN: 10049541     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1004-9541(06)60039-6     Document Type: Article

References (13)

1. Liang, J., Qian, J.X., Multivariate statistical process monitoring and control: Recent developments and applications 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 diagnosis 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 operation 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 missing link in multivariate quality control, Appt. Math. Comp. Sci., 8, 775-792 (1998).
6. Chiang, L.H., Pell, R.J., Genetic algorithms combined sswith discriminant analysis for key variable identification, 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 selection, 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., Parameters selection in gene selection using Gaussian Kernel 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 industrial-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).