Number of extracted features in advance and trial and

Info icon This preview shows pages 4–5. Sign up to view the full content.

number of extracted features in advance, and trial-and-error for determining the appropriate number of extracted features can then be avoided. Experiments on three real-world data sets have demonstrated that our method can run faster and obtain better extracted features than other methods. REFERENCES [1] 20Newsgroups/, 2010. [2] reuters21578/reuters21578. html. 2010. [3] H. Kim, P. Howland, and H. Park, “Dimension Reduction in Text Classification with Support Vector Machines,” J. Machine Learning Research, vol. 6, pp. 37-53, 2005. [4] F. Sebastiani, “Machine Learning in Automated Text Categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1-47, 2002. [5] B.Y. Ricardo and R.N. Berthier, Modern Information Retrieval. Addison Wesley Longman, 1999. [6] A.L. Blum and P. Langley, “Selection of Relevant Features and Examples in Machine Learning,” Aritficial Intelligence, vol. 97, nos. 1/2, pp. 245-271, 1997. [7] E.F. Combarro, E. Montanes, I. Dı´az, J. Ranilla, and R. Mones, “Introducing a Family of Linear Measures for Feature Selection in Text Categorization,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 9, pp. 1223-1232, Sept. 2005. [8] K. Daphne and M. Sahami, “Toward Optimal Feature Selection,” Proc. 13th Int’l Conf. Machine Learning, pp. 284-292, 1996. [9] R. Kohavi and G. John, “Wrappers for Feature Subset Selection,” Aritficial Intelligence, vol. 97, no. 1-2, pp. 273-324, 1997. [10] Y. Yang and J.O. Pedersen, “A Comparative Study on Feature Selection in Text Categorization,” Proc. 14th Int’l Conf. Machine Learning, pp. 412-420, 1997.
Image of page 4

Info icon This preview has intentionally blurred sections. Sign up to view the full version.

Sainani et al., International Journal of Advanced Research in Computer Science and Software Engineering 2 (8), August- 2012, pp. 258-262 © 2012, IJARCSSE All Rights Reserved Page | 262 [11] D.D. Lewis, “Feature Selection and Feature Extraction for Text Categorization,” Proc. Workshop Speech and Natural Language, pp. 212-217, 1992. [12] H. Li, T. Jiang, and K. Zang, “Efficient and Robust Feature Extraction by Maximum Margin Criterion,” T. Sebastian, S. Lawrence, and S. Bernhard eds. Advances in Neural Information Processing System, pp. 97-104, Springer, 2004. [13] E. Oja, Subspace Methods of Pattern Recognition. Research Studies Press, 1983. [14] J. Yan, B. Zhang, N. Liu, S. Yan, Q. Cheng, W. Fan, Q. Yang, W. Xi, and Z. Chen, “Effective and Efficient Dimensionality Reduction for Large-Scale and Streaming Data Prepro cessing,” IEEE Trans. Knowledge and Data Eng., vol. 18, no. 3, pp. 320-333, Mar. 2006. [15] I.T. Jolliffe, Principal Component Analysis. Springer-Verlag, 1986. [16] A.M. Martinez and A.C. Kak, “PCA versus LDA,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2 pp. 228-233, Feb. 2001. [17] H. Park, M. Jeon, and J. Rosen, “Lower Dimensional Representation of Text Data Based on Centroids and Least Squares,” BIT Numerical Math, vol. 43, pp. 427-448, 2003. [18] S.T. Roweis and L.K. Saul, “No nlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, pp. 2323-2326, 2000. [19] J.B. Tenenbaum, V. de Silva, and J.C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, vol. 290, pp. 2319-2323, 2000. [20] M. Belkin and P. Niyogi, “Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering,” Advances in Neural Information Processing Systems, vol. 14, pp. 585-591, The MIT Press 2002. [21] K. Hiraoka, K. Hidai, M. Hamahira, H. Mizoguchi, T. Mishima, and S. Yoshizawa, “Successive Learning of Linear Discriminant Analysis: Sanger- Type Algorithm,” Proc. IEEE CS Int’l Conf. Pattern Recognition, pp. 2664-2667, 2000.
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.
  • Fall '16
  • FIX
  • Machine Learning, The Land, International Journal of Advanced Research in Computer Science and Software Engineering, IEEE Trans, Text Classification, Sainani Arpitha

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern