Multiclass SVM-RFE for product form feature selection

Expert Systems with Applications

Volumn 35, Issue 1-2, 2008, Pages 531-541

  Shieh, Meng Dar ^a   Yang, Chih Chieh ^a  

^a NATIONAL CHENG KUNG UNIVERSITY   (Taiwan)

Author keywords

Feature selection; Mobile phone design; Multiclass support vector machines recursive feature elimination (SVM RFE)

Indexed keywords

ARCHITECTURAL DESIGN; DESIGN; FUZZY LOGIC; GEARS; IMAGE RETRIEVAL; KETONES; LEARNING SYSTEMS; MATHEMATICAL MODELS; MULTILAYER NEURAL NETWORKS; PRODUCT DESIGN; STRUCTURAL DESIGN; TELECOMMUNICATION EQUIPMENT; VECTORS;

(E ,3E) PROCESS; APPLIED (CO); CONSUMER PREFERENCES; CROSS-VALIDATION (CV); DISCRIMINATION CAPABILITY; ELSEVIER (CO); FEATURE SELECTION (FS); FORM FEATURES; FUZZY SUPPORT VECTOR MACHINES; GAUSSIAN KERNELS; GENERALIZATION ERRORS; MULTI CLASS; MULTICLASS SUPPORT VECTOR MACHINE (SVM); OPTIMAL TRAINING; PREDICTION MODELING; PRODUCT FORMS; RELATIVE IMPORTANCE; SVM-RFE; WEIGHT DISTRIBUTIONS;

SUPPORT VECTOR MACHINES;

EID: 44949258241     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.07.043     Document Type: Article

References (29)

1. Basak J., De R.K., and Pal S.K. Unsupervised feature selection using a neuro-fuzzy approach. Pattern Recognition Letters 19 (1998) 997-1006
2. Bradley, P. S. & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In Proceedings of 15th international conference on machine learning (pp. 82-90). California, San Francisco.
3. Burges C. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2 2 (1998) 955-974
4. Chen Y.W., and Lin C.J. Combining SVMs with various feature selection strategies. In: Guyon I., Gunn S., Nikravesh M., and Zadeh L. (Eds). Feature extraction, foundations and applications (2006), Springer, New York
5. Duan K.B., Rajapakse J.C., Wang H., and Azuaje F. Multiple SVM-RFE for gene selection in cancer classification with expression data. IEEE Transactions of Nanobioscience 4 3 (2005) 228-234
6. Evgeniou T., Pontil M., Papageorgiou C., and Poggio T. Image representation and feature selection for multimedia database search. IEEE Transactions of Knowledge Data Engineering 15 4 (2003) 911-920
7. Guyon I., Weston J., Barnhill S., and Bapnik V. Gene selection for cancer classification using support vector machines. Machine Learning 46 1-3 (2002) 389-422
8. Han S.H., and Kim J. A comparison of screening methods: selecting important design variables for modeling product usability. International Journal of Industrial Ergonomics 32 (2003) 189-198
9. Han S.H., Kim K.J., and Yun M.H. Identifying mobile phone design features critical to user satisfaction. Human Factors and Ergonomics in Manufacturing 14 1 (2004) 15-29
10. Han S.H., and Yang H. Screening important design variables for building a usability model. International Journal of Industrial Ergonomics 33 (2004) 159-171
11. Hsu, C. W., Chang, C. C. & Lin, C. J. (2003). Technical report, A practical guide to support vector classification, Department of Computer Science and Information Engineering, National Taiwan University: Taiwan.
12. Hsu C.W., and Lin C.J. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks 13 (2001) 415-425
13. Jensen, R. (2005). Combining rough and fuzzy sets for feature selection. School of Informatics, University of Edinburgh. Doctor of Philosophy.
14. Jindo T., Hirasago K., and Nagamachi M. Development of a design support system for office chairs using 3D graphics. International Journal of Industrial Ergonomics 15 (1995) 49-62
15. Juang B.H., and Katagiri S. Discriminative learning for minimum error classification. IEEE Transactions of Signal Process 40 12 (1992) 3043-3054
16. Kohavi R., and John G. Wrapper for feature subset selection. Artificial Intelligence 97 1-2 (1997) 273-324
17. Krebel U. Pairwise classification and support vector machines. In: Scholkopf B., Burges J.C., and Smola A.J. (Eds). Advances in kernel methods-support vector learning (1999), MIT Press, Cambridge, MA 255-268
18. Kwahk J., and Han S.H. A methodology for evaluating the usability of audiovisual consumer electronic products. Applied Ergonomics 33 (2002) 419-431
19. LeCun Y., Denker J., Solla S., Howard R., and Jackel L.D. Optimal brain damages. In: Touretzky D.S. (Ed). Advances in neural information processing systems (1990), Morgan Kaufman, CA, Mateo 598-605
20. Lin C.F., and Wang S.D. Fuzzy support vector machines. IEEE Transactions on Neural Networks 13 2 (2002) 464-471
21. Liu Y., and Zheng Y.F. FS_SFS: A novel feature selection method for support vector machines. Pattern Recognition 39 (2006) 1333-1345
22. Mao K.Z. Feature subset selection for support vector machines through discriminative function pruning analysis. IEEE Transactions of System, Man and Cybernetics 34 1 (2004) 60-67
23. Mao Y., Zhou X., Pi D., Sun Y., and Wong S.T.C. Multiclass cancer classification by using fuzzy support vector machine and binary decision tree with gene selection. Journal of Biomedicine and Biotechnology 2 (2005) 160-171
24. Pal, S. K., Basak, J. & De, R. K. (1996). Feature selection: A neuro-fuzzy approach. In Proceedings of the IEEE international conference on neural networks (pp. 1197-1202). Washington, DC.
25. Park J., and Han S.H. A fuzzy rule-based approach to modeling affective user satisfaction towards office chair design. International Journal of Industrial Ergonomics 34 (2004) 31-47
26. Rakotomamonjy A. Variable selection using SVM-based criteria. Journal of Machine Learning Research 3 (2003) 1357-1370
27. Shimizu Y., and Jindo T. A fuzzy logic analysis method for evaluating human sensitivities. International Journal of Industrial Ergonomics 15 (1995) 39-47
28. Wakaki, T., Itakura, H. & Tamura, M. (2004). Rough set-aided feature selection for automatic web-page classification. In Proceedings of the IEEE/WIC/ACM international conference on web intelligence (pp. 70- 76).
29. Wang W., Xu Z., and Lu W. Determination of the spread parameter in the Gaussian kernel for classification and regression. Neurocomputing 55 (2003) 643-663