1Member of Young Researchers Club, Islamic Azad University, Dehaghan Branch, Isfahan, Iran
2Associate Professor, Department of Computer Engineering and Information Technology, Payame Noor University
3Department of Computer Engineering, Islamic Azad University, Dezfoul Branch, Iran
4MS c, software engineering department
Today a significant part of available data is saved in text database or text documents. The most important thing is to organize these documents. One way to organize text documents is to classify them. To classify texts is to assign text documents to their actual categories. This has two main steps, i.e. feature- and learning algorithm selection. There have been several methods suggested to classify text documents. In this paper, we propose a combined method to do this more efficiently. When selecting features, the proposed method uses filtering in order to reduce complexity and it is implemented using naïve Bayes and decision tree categories. Results indicate advantages of this combined method to individual classifying.