Affiliations: School of Foreign Languages, Shanghai Jiaotong University, Shanghai, China
Correspondence:
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Corresponding author. Hui Cao, School of Foreign Languages, Shanghai Jiaotong University, 200240, Shanghai, China. E-mail: [email protected].
Abstract: The selection of big data attributes plays a positive role in the development of the network. At present, the attribute selection for big data is completed by detecting the attribute of data, which can not guarantee the accuracy of the selection. In this paper, a big data attribute selection method based on support vector machine (SVM) is proposed for distributed network fault diagnosis database. The method is used to mine big data in the distributed network fault diagnosis database, and calculate its attribute weights according to which complete attribute classification, so as to complete the selection if big data attributes. Experiments show that the proposed method improves the efficiency of big data attribute selection, and has certain practical value.
Keywords: Distributed network, fault diagnosis database, big data attribute
selection, support vector machine, fault diagnosis database
