期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：66
期号：1
出版社：Journal of Theoretical and Applied
摘要：The purpose of this research was to test the effectiveness of the Thai language document categorization model by using the features of reduction and machine learning techniques. The experimental results showed that the support vector machine algorithm used to classify Thai documents did so with a highest efficiency of 93.06%, followed by na�ve-bayes and decision tree at 86.80% and 76.51% respectively. Considering the parameter that had the best efficiency, support vector machine algorithm with 7000 features had the best performance at 93.90%. The features could be reduced significantly without affecting the performance of categorization.
