Automatic document classification has become an important task because of the continually increasing number of text documents with the users have to deal with. The aim of this paper is to develop a non-adaptive meta-classifier for text documents that has an increased classification accuracy. The developed meta-classifier is based on combining some SVM classifiers and a Naïve Bayes classifier. We proposed a new meta-classification method which takes into consideration the corresponding positions and confidence degrees obtained for all the classes. In this work we have tried to find, using Genetic Algorithms, the optimal weighting factors for the values returned by each classifier separately. Consequently, it is possible for the meta-classifier to select as the winner class, a class that is not hierarchized as the first one by any of the compounded classifiers. The experimental results have showed that the classification accuracy can be improved through the proposed method.
Text Classification and Performance Evaluation, SVM, Meta-classification, Genetic Algorithms
Radu G. CREŢULESCU, Daniel I. MORARIU, Macarie BREAZU, Lucian N. VINŢAN, "Weights Space Exploration Using Genetic Algorithms for Meta-classifier in Text Document Classification*", Studies in Informatics and Control, ISSN 1220-1766, vol. 21(2), pp. 147-154, 2012. https://doi.org/10.24846/v21i2y201204
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