Fuzzy Feature Clustering for Text Classification Using Sequence Classifier

  IJCOT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© - July Issue 2013 by IJCTT Journal
Volume-4 Issue-7                           
Year of Publication : 2013
Authors :Ambily Balaram

MLA

Ambily Balaram"Fuzzy Feature Clustering for Text Classification Using Sequence Classifier "International Journal of Computer Trends and Technology (IJCTT),V4(7):1993-1999 July Issue 2013 .ISSN 2231-2803.www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract: - Due to the rapid growth of Internet Technologies and the prosper of WWW, the volume of textual data is increasing more and more, thereby leading to the significance of text classification. Feature Clustering is a powerful method to reduce the dimensionality of feature vector for text classification. Text Classification is one of the important research issues in the field of text mining, where the documents are classified with supervised knowledge. This paper proposes a sequence classifier in a two stage approach combining Support Vector Machines (SVMs) and Conditional Random Fields (CRFs).It is (i) highly accurate, (ii) Scalable and (iii) Easy to use in Data mining approach. . The proposed model works efficiently and effectively with great performance and high - accuracy results.

 

References-
[1] 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.
[2] D.D. Lewis, “Feature Selection and Feature Extraction for Text Categorization,” Proc. Workshop Speech and Natural Language, pp. 212-217, 1992.
[3] L.D. Baker and A.McCallum, “Distributional Clustering of Words for Text Classification,” Proc. ACM SIGIR, pp. 96-103, 1998.
[4] Jung-Yi Jiang, Ren-Jia Liou, and Shie-Jue Lee, Member, IEEE A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification
[5] T. Joachims, “Text Categorization with Support Vector Machine: Learning with Many Relevant Features,” Technical Report LS-8- 23, Univ. of Dortmund, 1998.
[6] Y. Yang and X. Liu, “A Re-Examination of Text Categorization Methods,” Proc. ACM SIGIR, pp. 42-49, 1999.
[7] John Lafferty, Andrew McCallum, Fernando Pereira “Conditional Random Fields: Probabilistic Models For Segmenting And Labeling Sequence Data”

Keywords : — Conditional Random Fields, Support Vector Machine.