Commenced in January 2007
Paper Count: 31113
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%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1061627Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1315
 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
 G. Siyang, L. Quingrui, M. Lin, Meta-classifier in Text Classification, http://www. comp.nus.edu.sg/~zhouyong/papers/cs5228project.pdf
 W.-H. Lin , A. Houptmann, News Video Classification Using SVMbased Multimodal Classifier and Combination Strategies, 2003
 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
 B. Schoelkopf, A. Smola, "Learning with Kernels, Support Vector Machines", MIT Press, London, 2002.
 C. Nello, J. Swawe-Taylor, "An introduction to Support Vector Machines", Cambridge University Press, 2000.
 D. Morariu, L. Vintan, "A Better Correlation of the SVM kernel-s Parameters", Proceeding of the 5th RoEduNet International Conference, Sibiu, June 2006.
 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
 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
 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.
 Reuters Corpus: http://about.reuters.com/researchandstandards/corpus/. Released in November 2000.
 S. Chakrabarti, "Mining the Web- Discovering Knowledge from hypertext data", Morgan Kaufmann Press, 2003.