基于极限学习机的XML文档分类

摘要

研究基于极限学习机(ELM)的XML文档分类方法.为优化文档的相似性计算,在结构链接向量模型的基础上,提出一种改进的特征向量模型RS-VSM,将有效的结构化信息合并到向量模型中.应用ELM对XML文档进行分类,为提高ELM分类的准确率,提出一种基于投票机制的Voting-ELM算法.实验结果证明,该算法的分类效果较优.%This paper studies extensible Markup Language(XML) document classification method based on Extreme Learning Machine(ELM). On the basis of Structured Link Vector Model(SLVM), an optimized Reduced Structured Vector Space Model(RS-VSM) is proposed to incorporate structural information into feature vectors more efficiently. It applies ELM in the XML document classification to achieve good performance at extremely high speed. A voting-ELM algorithm is proposed to improve accuracy of ELM classifiers. Experimental results demonstrate that the voting-ELM classifiers can achieve better performance.