计算机工程与应用2017，Vol.53Issue(4)：19-24,6.DOI:10.3778/j.issn.1002-8331.1607-0331
基于双隐层极限学习机的模糊XML文档分类
Classification of fuzzy XML documents based on double hidden layer ELM
摘要
Abstract
With the arrival of the era of big data, the management of distributed and heterogeneous fuzzy XML data is also becoming more and more important. In the management of fuzzy XML data, the classification of fuzzy XML documents is the key problem. In order to study the classification for fuzzy XML documents, in this paper, a new ELM-based double hidden layer framework is proposed. The proposed architecture is divided into two main components:the feature extraction of fuzzy XML documents are performed using Extreme Learning Machine in first layer, and then use these characteristics to classify the fuzzy XML documents by KELM Kernel Extreme Learning Machine in second layer. Finally, the perfor-mance advantages of the proposed method are verified by experiments. Firstly, the parameters including the number of hidden neuron, and the constant parameter C and kernel parameter γ are investigated in detail. Compared with the tradi-tional single hidden layer ELM(Extreme Learning Machine)and SVM(Support Vector Machine)method, the classifica-tion accuracy has been greatly improved and the training time has been decreased by approach based on the double hidden layer ELM proposed in this paper.关键词
模糊/XML文档/分类/特征提取/极限学习机Key words
fuzzy/XML documents/classification/feature extracting/Extreme Learning Machine(ELM)分类
信息技术与安全科学引用本文复制引用
赵震,马宗民,张富,林晓庆..基于双隐层极限学习机的模糊XML文档分类[J].计算机工程与应用,2017,53(4):19-24,6.基金项目
国家自然科学基金(No.61370075,No.61073139,No.61202260) （No.61370075,No.61073139,No.61202260）
教育部新世纪优秀人才支持计划项目(No.NCET-05-0288). （No.NCET-05-0288）
