摘要
Web文本分类是Web数据挖掘的一个重要研究方向,它是在通过经验数据训练得到的分类体系下,根据网页的文本内容自动判别网页类别的过程,本文提出一种综合粗糙集与支持向量机的Web文本分类模型,利用粗糙集的属性约简方法,减少支持向量机训练数据的维数,提高Web文本分类的性能与效率.
The Web text classification is an important research direction in Web data mining. It obtains a classified system by training knowledge data, then according to homepage text content automatic distinct homepage category by using this system This article advances a Web text classification model which synthesis rough set and support vector machine. Using the rough set＇s attribute rednction method to reduce the dimension of support vector machine＇s training data, then enhances the Web text classification＇s performance and efficiency.
出处
《漳州师范学院学报（自然科学版）》
2009年第3期37-42,共6页
Journal of ZhangZhou Teachers College（Natural Science)
关键词
粗糙集
支持向量机
WEB文本分类
Rough Set
Support Vector Machine
Web text classification
