Individualized Automatic Classification of Web Documents

摘要：

This paper applies Na(i)ve Bayes classifier in designing customized automatic web document classification to systematically collecting massive news articles from the Internet. The proposed news classification system allows users to establish the necessary information classifications based on their own preferences. When the amount of daily news is increasing, this approach enables users to effectively filter through large amount of articles and more focused on interested articles. Performances of the proposed approach are characterized by the recall rate and precision. This system can achieve over 66％ recall rate, and over 89％ precision rate for a real-world Chinese test database.

关键词： Na(i)ve Bayes web documents classification

作者: Yihjia Tsai Kaun-Yu Chen

作者单位: Department of Computer Science and Information Engineering, Tamkang University, Taipei

会议类型: 国际会议

会议名称: 2010 Cross-Strait Conference on Information Science and Technology(2010 海峡两岸信息科学与技术学术交流会)

会议地点: 秦皇岛

会议语种:英文

页码: 410-412

在线出版日期: 2010-07-09（万方平台首次上网日期，不代表论文的发表时间）