@article{(Open Science Index):https://publications.waset.org/pdf/5749,
	  title     = {Meta-Classification using SVM Classifiers for Text Documents},
	  author    = {Daniel I. Morariu and  Lucian N. Vintan and  Volker Tresp},
	  country	= {},
	  institution	= {},
	  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%.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {9},
	  year      = {2008},
	  pages     = {3166 - 3171},
	  ee        = {https://publications.waset.org/pdf/5749},
	  url   	= {https://publications.waset.org/vol/21},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 21, 2008},
	}