Meta-Classification using SVM Classifiers for Text Documents
Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.
[1] N. Dimitrova, L. Agnihotri and G. Wei, Video Classification Based on
HMM Using Text and Face, Proceedings of the European Conference on
Signal Processing, Finland, 2000
[2] G. Siyang, L. Quingrui, M. Lin, Meta-classifier in Text Classification,
http://www. comp.nus.edu.sg/~zhouyong/papers/cs5228project.pdf
[3] W.-H. Lin , A. Houptmann, News Video Classification Using SVMbased
Multimodal Classifier and Combination Strategies, 2003
[4] W.-H. Lin , R. Jin, A. Houptmann, A Meta-classification of Multimedia
Classifiers, International Workshop on Knowledge Discovery in
Multimedia and Complex Data, Taiwan, 2002
[5] B. Schoelkopf, A. Smola, "Learning with Kernels, Support Vector
Machines", MIT Press, London, 2002.
[6] C. Nello, J. Swawe-Taylor, "An introduction to Support Vector
Machines", Cambridge University Press, 2000.
[7] D. Morariu, L. Vintan, "A Better Correlation of the SVM kernel-s
Parameters", Proceeding of the 5th RoEduNet International Conference,
Sibiu, June 2006.
[8] D. Morariu, L. Vintan, V. Tresp, Feature Selection Methods for an
Improved SVM Classifier, Proceedings of the 14th International
Conference on Computational and Information Science, pp. 83-89,
Prague, August 2006
[9] D. Morariu, L. Vintan, V. Tresp, Evolutionary Feature Selection for Text
Documents Using the SVM , Submitted to The 3rd International
Conference on Neural Computing and Patter Recognition, October 2006
[10] D. Morariu, "Classification and Clustering using Support Vector
Machine", 2nd PhD Report, University ÔÇ×Lucian Blaga" of Sibiu,
September, 2005, http://webspace.ulbsibiu.ro/ daniel.morariu/html/Docs
/Report2.pdf.
[11] Reuters Corpus: http://about.reuters.com/researchandstandards/corpus/.
Released in November 2000.
[12] S. Chakrabarti, "Mining the Web- Discovering Knowledge from
hypertext data", Morgan Kaufmann Press, 2003.
[1] N. Dimitrova, L. Agnihotri and G. Wei, Video Classification Based on
HMM Using Text and Face, Proceedings of the European Conference on
Signal Processing, Finland, 2000
[2] G. Siyang, L. Quingrui, M. Lin, Meta-classifier in Text Classification,
http://www. comp.nus.edu.sg/~zhouyong/papers/cs5228project.pdf
[3] W.-H. Lin , A. Houptmann, News Video Classification Using SVMbased
Multimodal Classifier and Combination Strategies, 2003
[4] W.-H. Lin , R. Jin, A. Houptmann, A Meta-classification of Multimedia
Classifiers, International Workshop on Knowledge Discovery in
Multimedia and Complex Data, Taiwan, 2002
[5] B. Schoelkopf, A. Smola, "Learning with Kernels, Support Vector
Machines", MIT Press, London, 2002.
[6] C. Nello, J. Swawe-Taylor, "An introduction to Support Vector
Machines", Cambridge University Press, 2000.
[7] D. Morariu, L. Vintan, "A Better Correlation of the SVM kernel-s
Parameters", Proceeding of the 5th RoEduNet International Conference,
Sibiu, June 2006.
[8] D. Morariu, L. Vintan, V. Tresp, Feature Selection Methods for an
Improved SVM Classifier, Proceedings of the 14th International
Conference on Computational and Information Science, pp. 83-89,
Prague, August 2006
[9] D. Morariu, L. Vintan, V. Tresp, Evolutionary Feature Selection for Text
Documents Using the SVM , Submitted to The 3rd International
Conference on Neural Computing and Patter Recognition, October 2006
[10] D. Morariu, "Classification and Clustering using Support Vector
Machine", 2nd PhD Report, University ÔÇ×Lucian Blaga" of Sibiu,
September, 2005, http://webspace.ulbsibiu.ro/ daniel.morariu/html/Docs
/Report2.pdf.
[11] Reuters Corpus: http://about.reuters.com/researchandstandards/corpus/.
Released in November 2000.
[12] S. Chakrabarti, "Mining the Web- Discovering Knowledge from
hypertext data", Morgan Kaufmann Press, 2003.
@article{"International Journal of Information, Control and Computer Sciences:54839", author = "Daniel I. Morariu and Lucian N. Vintan and Volker Tresp", title = "Meta-Classification using SVM Classifiers for Text Documents", abstract = "Text categorization is the problem of classifying text
documents into a set of predefined classes. In this paper, we
investigated three approaches to build a meta-classifier in order to
increase the classification accuracy. The basic idea is to learn a metaclassifier
to optimally select the best component classifier for each
data point. The experimental results show that combining classifiers
can significantly improve the accuracy of classification and that our
meta-classification strategy gives better results than each individual
classifier. For 7083 Reuters text documents we obtained a
classification accuracies up to 92.04%.", keywords = "Meta-classification, Learning with Kernels, Support
Vector Machine, and Performance Evaluation.", volume = "2", number = "9", pages = "2994-6", }