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The Scientific World Journal
Volume 2014, Article ID 795624, 10 pages
http://dx.doi.org/10.1155/2014/795624
Research Article

SVM-RFE Based Feature Selection and Taguchi Parameters Optimization for Multiclass SVM Classifier

1Department of Industrial Engineering and Management, National Chin-Yi University of Technology, No. 57, Sec. 2, Zhong-Shan Road, Taiping District, Taichung 41170, Taiwan
2Department of Industrial Engineering & Management, National Chiao-Tung University, No. 1001, Ta-Hsueh Road, Hsinchu 300, Taiwan

Received 20 June 2014; Revised 5 August 2014; Accepted 5 August 2014; Published 10 September 2014

Academic Editor: Shifei Ding

Copyright © 2014 Mei-Ling Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.
  2. J. Luts, F. Ojeda, R. van de Plas Raf, B. de Moor, S. van Huffel, and J. A. K. Suykens, “A tutorial on support vector machine-based methods for classification problems in chemometrics,” Analytica Chimica Acta, vol. 665, no. 2, pp. 129–145, 2010. View at Publisher · View at Google Scholar · View at Scopus
  3. M. F. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis,” Expert Systems with Applications, vol. 36, no. 2, pp. 3240–3247, 2009. View at Publisher · View at Google Scholar · View at Scopus
  4. C.-Y. Chang, S.-J. Chen, and M.-F. Tsai, “Application of support-vector-machine-based method for feature selection and classification of thyroid nodules in ultrasound images,” Pattern Recognition, vol. 43, no. 10, pp. 3494–3506, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  5. H.-L. Chen, B. Yang, J. Liu, and D.-Y. Liu, “A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis,” Expert Systems with Applications, vol. 38, no. 7, pp. 9014–9022, 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. P. Danenas and G. Garsva, “Credit risk evaluation modeling using evolutionary linear SVM classifiers and sliding window approach,” Procedia Computer Science, vol. 9, pp. 1324–1333, 2012. View at Google Scholar
  7. C. L. Huang, H. C. Liao, and M. C. Chen, “Prediction model building and feature selection with support vector machines in breast cancer diagnosis,” Expert Systems with Applications, vol. 34, no. 1, pp. 578–587, 2008. View at Publisher · View at Google Scholar · View at Scopus
  8. H. F. Liau and D. Isa, “Feature selection for support vector machine-based face-iris multimodal biometric system,” Expert Systems with Applications, vol. 38, no. 9, pp. 11105–11111, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. Y. Zhang, Z. Chi, and Y. Sun, “A novel multi-class support vector machine based on fuzzy theories,” in Intelligent Computing: International Conference on Intelligent Computing, Part I (ICIC '06), D. S. Huang, K. Li, and G. W. Irwin, Eds., vol. 4113 of Lecture Notes in Computer Science, pp. 42–50, Springer, Berlin, Germany. View at Publisher · View at Google Scholar
  10. Y. Aksu, D. J. Miller, G. Kesidis, and Q. X. Yang, “Margin-maximizing feature elimination methods for linear and nonlinear kernel-based discriminant functions,” IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 701–717, 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. P. Pudil, J. Novovičová, and J. Kittler, “Floating search methods in feature selection,” Pattern Recognition Letters, vol. 15, no. 11, pp. 1119–1125, 1994. View at Publisher · View at Google Scholar · View at Scopus
  12. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, “Gene selection for cancer classification using support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 389–422, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Harikrishna, M. A. H. Farquad, and Shabana, “Credit scoring using support vector machine: a comparative analysis,” in Advanced Materials Research, Trans Tech Publications, Zürich, Switzerland, 2012. View at Google Scholar
  14. X. Lin, F. Yang, L. Zhou et al., “A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information,” Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, vol. 10, pp. 149–155, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Zhang and M. Jianwen, “Feature selection for hyperspectral data based on recursive support vector machines,” International Journal of Remote Sensing, vol. 30, no. 14, pp. 3669–3677, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. X. Xie, Q. H. Hu, and D. R. Yu, “Fuzzy output support vector machines for classification,” in Advances in Natural Computation, L. Wang, K. Chen, and Y. S. Ong, Eds., vol. 3612, pp. 1190–1197, Springer, Berlin, Germany.
  17. Y. Liu, Z. You, and L. Cao, “A novel and quick SVM-based multi-class classifier,” Pattern Recognition, vol. 39, no. 11, pp. 2258–2264, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  18. J. Platt, N. C. Cristianini, and J. Shawe-Taylor, “Large margin DAGs for multiclass classification,” in Advances in Neural Information Processing Systems, S. A. Solla, T. K. Leen, and K. R. Muller, Eds., vol. 12, pp. 547–553, 2000. View at Google Scholar
  19. Y. Xu, S. Zomer, and R. G. Brereton, “Support vector machines: a recent method for classification in chemometrics,” Critical Reviews in Analytical Chemistry, vol. 36, no. 3-4, pp. 177–188, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. M. L. Huang, Y. H. Hung, and E. J. Lin, “Effects of SVM parameter optimization based on the parameter design of Taguchi method,” International Journal on Artificial Intelligence Tools, vol. 20, no. 3, pp. 563–575, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. H.-C. Lin, C.-T. Su, C.-C. Wang, B.-H. Chang, and R.-C. Juang, “Parameter optimization of continuous sputtering process based on Taguchi methods, neural networks, desirability function, and genetic algorithms,” Expert Systems with Applications, vol. 39, no. 17, pp. 12918–12925, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Mao, D. Pi, Y. Liu, and Y. Sun, “Accelerated recursive feature elimination based on support vector machine for key variable identification,” Chinese Journal of Chemical Engineering, vol. 14, no. 1, pp. 65–72, 2006. View at Publisher · View at Google Scholar · View at Scopus
  23. A. Pal and J. Maiti, “Development of a hybrid methodology for dimensionality reduction in Mahalanobis-Taguchi system using Mahalanobis distance and binary particle swarm optimization,” Expert Systems with Applications, vol. 37, no. 2, pp. 1286–1293, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. C.-T. Su and Y.-H. Hsiao, “Multiclass MTS for simultaneous feature selection and classification,” IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 2, pp. 192–205, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. X. Lin, F. Yang, L. Zhou et al., “A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information,” Journal of Chromatography B, vol. 910, pp. 149–155, 2012. View at Publisher · View at Google Scholar · View at Scopus
  26. E. Hüllermeier and S. Vanderlooy, “Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting,” Pattern Recognition, vol. 43, no. 1, pp. 128–142, 2010. View at Publisher · View at Google Scholar · View at Scopus
  27. L. Bottou, C. Cortes, J. Denker et al., “Comparison of classifier methods—a case study in handwritten digit recognition,” in Proceedings of the 12th Iapr International Conference on Pattern Recognition, vol. 2, pp. 77–82, IEEE Computer Society Press, Los Alamitos, Calif, USA, 1994.
  28. J. Furnkranz, “Round robin rule learning,” in Proceedings of the 18th International Conference on Machine Learning (ICML ’01), pp. 146–153, 2001.
  29. M. R. Sohrabi, S. Jamshidi, and A. Esmaeilifar, “Cloud point extraction for determination of Diazinon: optimization of the effective parameters using Taguchi method,” Chemometrics and Intelligent Laboratory Systems, vol. 110, no. 1, pp. 49–54, 2012. View at Publisher · View at Google Scholar · View at Scopus
  30. W. C. Hsu and T. Y. Yu, “Support vector machines parameter selection based on combined taguchi method and staelin method for e-mail spam filtering,” International Journal of Engineering and Technology Innovation, vol. 2, no. 2, pp. 113–125, 2012. View at Google Scholar
  31. J. Arenas-García and F. Pérez-Cruz, “Multi-class support vector machines: A new approach,” in Proceeding of the IEEE International Conference on Accoustics, Speech, and Signal Processing (ICASSP ’03), vol. 2, pp. 781–784, April 2003. View at Publisher · View at Google Scholar · View at Scopus
  32. K. G. Srinivasa, K. R. Venugopal, and L. M. Patnaik, “Feature extraction using fuzzy c-means clustering for data mining systems,” International Journal of Computer Science and Network Security, vol. 6, no. 3A, pp. 230–236, 2006. View at Google Scholar
  33. Y. Ren, H. Liu, C. Xue, X. Yao, M. Liu, and B. Fan, “Classification study of skin sensitizers based on support vector machine and linear discriminant analysis,” Analytica Chimica Acta, vol. 572, no. 2, pp. 272–282, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. He, Farthest-point heuristic based initialization methods for K-modes clustering [thesis], Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, China, 2006.
  35. S. Golzari, S. Doraisamy, M. N. Sulaiman, and N. I. Udzir, “Effect of fuzzy resource allocation method on AIRS classifier accuracy,” Journal of Theoretical and Applied Information Technology, vol. 5, no. 1, pp. 18–24, 2009. View at Google Scholar