%0 Journal Article
	%A Daniel I. Morariu and  Lucian N. Vintan and  Volker Tresp
	%D 2008
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 21, 2008
	%T Meta-Classification using SVM Classifiers for Text Documents
	%U https://publications.waset.org/pdf/5749
	%V 21
	%X 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%.
	%P 3166 - 3171