关键词: 模糊, XML文档, 分类, 特征提取, 极限学习机

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 performance 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 traditional single hidden layer ELM（Extreme Learning Machine） and SVM（Support Vector Machine） method, the classification 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.

Key words: fuzzy, XML documents, classification, feature extracting, Extreme Learning Machine（ELM）