Research Journal of Applied Sciences

Year: 2014
Volume: 9
Issue: 4
Page No. 208 - 213

Double Linear Support Vector Machine for Dimensionality Reduction

Authors : Wipawan Buathong and Phayung Meesad

References

Buathong, W. and P. Meesad, 2013. Enhancing the efficiency of dimensionality reduction using a combined linear svm weight with reliefF feature selection method. Proceedings of the 9th International Conference on Computing and Information Technology, May 9-10, 2013, Bangkok, pp: 125-134.

Buathong, W., 2012. A comparison of dimensionality reduction techniques using information gain, gain ratio and linear SVM weights ranking methods. Proceedings of the 5th ACTIS National Conference on Applied Computer Technology and Information Systems, September 2012, Songkhla, pp: 185-189.

Buathong, W., K. Suwannaraj and W. Channasap, 2012. Effect of data dimension reduction analysis and data classification performance of decision tree, support vector machine and naive bayes. Proceedings of the 8th National Conference on Computing and Information Technology, May 3-Jun 1, 2012, Bangkok, pp: 568-573.

Campbell, C., C. Lin, S. Keerthi and A.V. Sanchez, 2003. Special issue on support vector machines. Neurocomputing, 55: 1-3.

Chang, Y.W. and C.J. Lin, 2008. Feature ranking using linear SVM. J. Mach. Learn. Res. Proc., 3: 53-64.
Direct Link  |  

Daintith, J. and E. Wright, 2006. The Facts on File Dictionary of Computer Science. 2nd Edn., Facts On File, New York, ISBN: 9780816059997, Pages: 273.

Dash, M. and H. Liu, 1997. Feature selection for classification. Intell. Data Anal., 1: 131-156.
CrossRef  |  Direct Link  |  

Deng, H., G. Runger and E. Tuv, 2011. Bias of importance measures for multivalued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks, June 14-17, 2011, Berlin, Heidelberg, pp: 293-300.

Downing, D.A., M.A. Covington, M.M. Covington and C.A. Covington, 2009. Dictionary of Computer and Internet Terms. 10th Edn., Barron's, New York.

Guyon, I. and A. Elisseeff, 2003. An introduction to variable and feature selection. J. Mach. Learn. Res., 3: 1157-1182.
Direct Link  |  

Harris, E., 2002. Information gain versus gain ratio: A study of split method biases. http://rutcor.rutgers.edu/~amai/aimath02/PAPERS/14.pdf.

Hsu, C.W. and C.J. Lin, 2002. A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Networks, 13: 415-425.
CrossRef  |  

Jin, X., R. Li, X. Shen and R. Bie, 2007. Automatic web pages categorization with relieff and hidden naive bayes. Proceedings of the ACM Symposium on Applied Computing, March 11-15, 2007, Korea, pp: 617-621.

Pongpatharakan, P., 2009. A comparative study of classification properties between cart, SVM, c5.0 and hybrid methods. Proceedings of the 5th National Conference on Computing and Information Technology, August 23-28, 2009, Atlanta GA., pp: 1102-1106.

Sarrafzadeh, A., H.A. Atabay, M.M. Pedram and J. Shanbehzadeh, 2012. ReliefF based feature selection in content-based image retrieval. Proceedings of the International Multi Conference on Engineering and Computer Science, Volume 1, March 14-16, 2012, Hong Kong, pp: 19-22.

Shukran, M.A.M., O. Zakaria, N.W. Ahmad and M.A. Zaidi, 2011. A classification method for data mining using Svm-weight and euclidean distance. Aust. J. Basic Applied Sci., 5: 2053-2059.
Direct Link  |  

Sriurai, W., P. Meesad and C. Haruchaiyasak, 2009. A topic-model based feature processing for text categorization. Proceedings of the 5th National Conference on Computing and Information Technology, August 23-28, 2009, Atlanta GA., pp: 146-151.

Sun, Y. and D. Wu, 2008. A relief based feature extraction algorithm. Proceedings of the SIAM International Conference on Data Mining, April 24-26, 2008, Atlanta, Georgia, pp: 188-195.

Tan, P.N., M. Steibach and V. Kumar, 2006. Introduction to Data Mining. Pearson Addison Wesley, Boston, MA., USA., ISBN-13: 9780321420527, Pages: 769.

Vapnik, V.N., 1995. The Nature of Statistical Learning Theory. 1st Edn., Springer-Verlag, New York, USA.

Weston, J., S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio and V. Vapnik, 2001. Feature Selection for SVMs. In: Advances in Neural Information Processing Systems 13, Leen, T.K., T.G. Dietterich and V. Tresp (Eds.). MIT Press, USA., pp: 668-674.

Zhang, H., L. Jiang and J. Su, 2005. Hidden Naive Bayes. Proceedings of the 20th National Conference on Artificial Intelligence, Volume 2, July 9-13, 2005, Pittsburgh, Pennsylvania, pp: 919-924.

Zhang, Y., C. Ding and T. Li, 2007. A two-stage gene selection algorithm by combining relieff and mrmr. Proceedings of the 7th IEEE International Symposium on Bioinformatics and Bioengineering, October 14-17, 2007, Boston, pp: 164-171.

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