Abstract
Recently, the size of online texts and textual information is increasing explosively, and the automated classification has a great potential for handling data such as news materials and images. Text classification system is based on supervised learning which needs laborous work by human expert. The main goal of this paper is to reduce the manual intervention, required for the task. The other goal is to increase accuracy to be high. Most of the documents have high complexity in contents and the high similarities in their described style. So, the classification results are not satisfactory. This paper shows the implementation of classification system based on ETOM+RPost algorithm and classification progress using SPAM data. In experiments, we verified our system with right-training documents and wrong-training documents. The experimental results show that our system has high accuracy and stability in all situation as 16% improvement in accuracy.
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