- Tytuł:
- Research on big data attribute selection method in submarine optical fiber network fault diagnosis database
- Autorzy:
- Chen, G.
- Powiązania:
- https://bibliotekanauki.pl/articles/259788.pdf
- Data publikacji:
- 2017
- Wydawca:
- Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
- Tematy:
-
submarine optical fiber network
fault diagnosis database
big data attribute selection - Opis:
- At present, in the fault diagnosis database of submarine optical fiber network, the attribute selection of large data is completed by detecting the attributes of the data, the accuracy of large data attribute selection cannot be guaranteed. In this paper, a large data attribute selection method based on support vector machines (SVM) for fault diagnosis database of submarine optical fiber network is proposed. Mining large data in the database of optical fiber network fault diagnosis, and calculate its attribute weight, attribute classification is completed according to attribute weight, so as to complete attribute selection of large data. Experimental results prove that ,the proposed method can improve the accuracy of large data attribute selection in fault diagnosis database of submarine optical fiber network, and has high use value.
- Źródło:
-
Polish Maritime Research; 2017, S 3; 121-127
1233-2585 - Pojawia się w:
- Polish Maritime Research
- Dostawca treści:
- Biblioteka Nauki
Artykuł