文章基本信息

标题：Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification
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作者：Jafar Ababneh
期刊名称：Modern Applied Science
印刷版ISSN：1913-1844
电子版ISSN：1913-1852
出版年度：2019
卷号：13
期号：11
页码：31-36
DOI：10.5539/mas.v13n11p31
语种：English
出版社：Canadian Center of Science and Education
摘要：Nowadays, many applications that use large data have been developed due to the existence of the Internet of Things. These applications are translated into different languages and require automated text classification (ATC). The ATC process depends on the content of one or more predefined classes. However, this process is problematic for the Arabic translation of the data. This study aims to solve this issue by investigating the performances of three classification algorithms, namely, k-nearest neighbor (KNN), decision tree (DT), and nave Bayes (NB) classifiers, on Saudi Press Agency datasets. Results showed that the NB algorithm outperformed DT and KNN algorithms in terms of precision, recall, and F1. In future works, a new algorithm that can improve the handling of the ATC problem will be developed.
关键词：arabic data sets; data mining; decision tree; K-nearest neighbors; NB algorithms; text classification; term weighting