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Meta-Classification using SVM Classifiers for Text Documents

Authors: Daniel I. Morariu, Lucian N. Vintan, Volker Tresp


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: Support Vector Machine, learning with kernels, Meta-classification, and Performance Evaluation

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