A feature extraction method for XML documents based on PCA. Issue 1 (1st December 2022)

• Record Type: Journal Article
• Authors: Yu, Zhiwen; Zhao, Ruifeng; Zeng, Kaiwen; Zheng, Wenjie; Liu, Hao; Yang, Ting
• Is Part Of: Journal of physics. Volume 2384 Issue 1(2022)
• Publication Date: 2022-12-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2384/1/012028
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: XML document stores the information of the new power system source load interaction. It has the characteristics of self-description, extensibility, structure, and content, which makes it widely used. Improving the method of extracting elements from XML documents is very helpful in solving the problem of distributed object operation measurement in the power grid. To classify or analyze XML documents better, based on the theoretical analysis of principal component analysis and the study of the text representation model, this paper proposes two methods: vectorization representation of XML documents and further feature extraction. The experiment result shows that the method of all path feature extraction for XML document can represent the main feature of XML document effectively, and is an important work for latter handling XML documents of power grid efficiently