
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
JungYi Jiang, RenJia Liou, ShieJue Lee, "A Fuzzy SelfConstructing Feature Clustering Algorithm for Text Classification," IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 3, pp. 335349, March, 2011.  
BibTex  x  
@article{ 10.1109/TKDE.2010.122, author = {JungYi Jiang and RenJia Liou and ShieJue Lee}, title = {A Fuzzy SelfConstructing Feature Clustering Algorithm for Text Classification}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {23}, number = {3}, issn = {10414347}, year = {2011}, pages = {335349}, doi = {http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.122}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Knowledge and Data Engineering TI  A Fuzzy SelfConstructing Feature Clustering Algorithm for Text Classification IS  3 SN  10414347 SP335 EP349 EPD  335349 A1  JungYi Jiang, A1  RenJia Liou, A1  ShieJue Lee, PY  2011 KW  Fuzzy similarity KW  feature clustering KW  feature extraction KW  feature reduction KW  text classification. VL  23 JA  IEEE Transactions on Knowledge and Data Engineering ER   
[1] Http://people.csail.mit.edu/jrennie20Newsgroups /, 2010.
[2] Http://kdd.ics.uci.edu/databases/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. 3753, 2005.
[4] F. Sebastiani, "Machine Learning in Automated Text Categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 147, 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. 245271, 1997.
[7] E.F. Combarro, E. Montañés, I. Díaz, 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. 12231232, Sept. 2005.
[8] K. Daphne and M. Sahami, "Toward Optimal Feature Selection," Proc. 13th Int'l Conf. Machine Learning, pp. 284292, 1996.
[9] R. Kohavi and G. John, "Wrappers for Feature Subset Selection," Aritficial Intelligence, vol. 97, no. 12, pp. 273324, 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. 412420, 1997.
[11] D.D. Lewis, "Feature Selection and Feature Extraction for Text Categorization," Proc. Workshop Speech and Natural Language, pp. 212217, 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. 97104, 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 LargeScale and Streaming Data Preprocessing," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 3, pp. 320333, Mar. 2006.
[15] I.T. Jolliffe, Principal Component Analysis. SpringerVerlag, 1986.
[16] A.M. Martinez and A.C. Kak, "PCA versus LDA," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2 pp. 228233, 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. 427448, 2003.
[18] S.T. Roweis and L.K. Saul, "Nonlinear Dimensionality Reduction by Locally Linear Embedding," Science, vol. 290, pp. 23232326, 2000.
[19] J.B. Tenenbaum, V. de Silva, and J.C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, vol. 290, pp. 23192323, 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. 585591, The MIT Press 2002.
[21] K. Hiraoka, K. Hidai, M. Hamahira, H. Mizoguchi, T. Mishima, and S. Yoshizawa, "Successive Learning of Linear Discriminant Analysis: SangerType Algorithm," Proc. IEEE CS Int'l Conf. Pattern Recognition, pp. 26642667, 2000.
[22] J. Weng, Y. Zhang, and W.S. Hwang, "Candid CovarianceFree Incremental Principal Component Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 10341040, Aug. 2003.
[23] J. Yan, B.Y. Zhang, S.C. Yan, Z. Chen, W.G. Fan, Q. Yang, W.Y. Ma, and Q.S. Cheng, "IMMC: Incremental Maximum Margin Criterion," Proc. 10th ACM SIGKDD, pp. 725730, 2004.
[24] L.D. Baker and A. McCallum, "Distributional Clustering of Words for Text Classification," Proc. ACM SIGIR, pp. 96103, 1998.
[25] R. Bekkerman, R. ElYaniv, N. Tishby, and Y. Winter, "Distributional Word Clusters versus Words for Text Categorization," J. Machine Learning Research, vol. 3, pp. 11831208, 2003.
[26] M.C. Dalmau and O.W.M. Flórez, "Experimental Results of the Signal Processing Approach to Distributional Clustering of Terms on Reuters21578 Collection," Proc. 29th European Conf. IR Research, pp. 678681, 2007.
[27] I.S. Dhillon, S. Mallela, and R. Kumar, "A Divisive InfomationTheoretic Feature Clustering Algorithm for Text Classification," J. Machine Learning Research, vol. 3, pp. 12651287, 2003.
[28] D. Ienco and R. Meo, "Exploration and Reduction of the Feature Space by Hierarchical Clustering," Proc. SIAM Conf. Data Mining, pp. 577587, 2008.
[29] N. Slonim and N. Tishby, "The Power of Word Clusters for Text Classification," Proc. 23rd European Colloquium on Information Retrieval Research (ECIR), 2001.
[30] F. Pereira, N. Tishby, and L. Lee, "Distributional Clustering of English Words," Proc. 31st Ann. Meeting of ACL, pp. 183190, 1993.
[31] H. AlMubaid and S.A. Umair, "A New Text Categorization Technique Using Distributional Clustering and Learning Logic," IEEE Trans. Knowledge and Data Eng., vol. 18, no. 9, pp. 11561165, Sept. 2006.
[32] G. Salton and M.J. McGill, Introduction to Modern Retrieval. McGrawHill Book Company, 1983.
[33] T. Joachims, "A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization," Proc. 14th Int'l Conf. Machine Learning, pp. 143151, 1997.
[34] J. Yen and R. Langari, Fuzzy LogicIntelligence, Control, and Information. PrenticeHall, 1999.
[35] J.S. Wang and C.S.G. Lee, "SelfAdaptive Neurofuzzy Inference Systems for Classification Applications," IEEE Trans. Fuzzy Systems, vol. 10, no. 6, pp. 790802, Dec. 2002.
[36] T. Joachims, "Text Categorization with Support Vector Machine: Learning with Many Relevant Features," Technical Report LS823, Univ. of Dortmund, 1998.
[37] C. Cortes and V. Vapnik, "SupportVector Network," Machine Learning, vol. 20, no. 3, pp. 273297, 1995.
[38] B. Schölkopf and A.J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2001.
[39] J. ShaweTaylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge Univ. Press, 2004.
[40] D.D. Lewis, Y. Yang, T. Rose, and F. Li, "RCV1: A New Benchmark Collection for Text Categorization Research," J. Machine Learning Research, vol. 5, pp. 361397, http://www.jmlr.org/papers/volume5/lewis04a lewis04a.pdf, 2004.
[41] The Cadê Web directory, http:/www.cade.com.br/, 2010.
[42] C.C. Chang and C.J. Lin, "Libsvm: A Library for Support Vector Machines," http://www.csie.ntu.edu.tw/~cjlinlibsvm. 2001.
[43] Y. Yang and X. Liu, "A ReExamination of Text Categorization Methods," Proc. ACM SIGIR, pp. 4249, 1999.
[44] G. Tsoumakas, I. Katakis, and I. Vlahavas, "Mining MultiLabel Data," Data Mining and Knowledge Discovery Handbook, O. Maimon and L. Rokach eds., second ed. Springer, 2009.
[45] Http://web.ist.utl.pt/~acardosodata sets /, 2010.