Title :
Network fault diagnosis based on rough set-support vector machine
Author :
Jiangwei, Guo ; Xiaoping, Wu ; Qing, Ye
Author_Institution :
Dept. of Inf. Security, Naval Univ. of Eng., Wuhan, China
Abstract :
At present, the technique of network fault diagnosis has been a very hot research domain. The scholars from both domestic and abroad have put forward many diagnosis approaches, but many of which have some disadvantages in dealing with uncertain problems. This paper proposes a rough set-support vector machine algorithm after studying the rough set and the support vector machine theories. In order to reduce the dimensions of the classification space, the algorithm first diminishes the attributes of the faults by means of the rough set theory (RST), thus improves the effect of the classification of the support vector machine (SVM).
Keywords :
fault diagnosis; pattern classification; rough set theory; support vector machines; classification space; network fault diagnosis; rough set-support vector machine; uncertain problems; Monitoring; network fault diagnosis; rough set theory; support vector machine;
Conference_Titel :
Computer Application and System Modeling (ICCASM), 2010 International Conference on
Conference_Location :
Taiyuan
Print_ISBN :
978-1-4244-7235-2
Electronic_ISBN :
978-1-4244-7237-6
DOI :
10.1109/ICCASM.2010.5622291