Abstract: 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.

Key words: eXtensible Markup Language(XML), classification, Extreme Learning Machine(ELM), Structured Link Vector Model(SLVM), voting mechanism

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。

关键词: 可扩展标记语言, 分类, 极限学习机, 结构链接向量模型, 投票机制

CLC Number: 

• TP18