摘要
Web文本分类技术是数据挖掘中一个研究热点领域,而支持向量机又是一种高效的分类识别方法,在解决高维模式识别问题中表现出许多特有的优势。文章通过分析Web文本的特点,研究了向量空间模型(VSM)的分类方法和核函数的选取,在此基础上结合决策树方法提出了一种基于决策树支持向量机的Web文本分类模型,并给出具体的算法。通过实验测试表明,该方法训练数据规模大大减少,训练效率较高,同时具有较好的精确率(90.11%)和召回率(89.38%)。
Web document classification has been considered as a hot research area in data mining. SVM is an effective method for learning the classification knowledge from massive data, especially in the situation of high cost in getting labeled classical examples. In this paper, based on the analyses of features of Web documents, this paper does research the approach of classification in Vector Space Model and select of Kernel function. Furthermore, a Web page classification model and algorithm that is based on Decision Tree SVM is presented. The experiments show that it not only reduces the size of train set, but also has very high training efficiency. Its precision（90.11%）and recall （89.38%）are also very good.
出处
《微电子学与计算机》
CSCD
北大核心
2006年第9期102-104,共3页
Microelectronics & Computer
基金
中国矿业大学青年科研基金项目(OD4490)
关键词
支持向量机
特征提取
WEB文本
文本分类
Support vector machine, Feature selection, Web documents, Text classification
