Network Fault Diagnosis Using Hierarchical SVMs Based on Kernel Method

Author

Zhang, Li ; Meng, Xiangru ; Zhou, Hua

Author_Institution

Telecommun. Eng. Inst., AFEU, Xi´´an

fYear

2009

fDate

23-25 Jan. 2009

Firstpage

753

Lastpage

756

Abstract

A new method based on kernel which can measure class separability in feature space is proposed in this paper for existing error accumulation when the hierarchical SVMs is used to diagnose multiclass network fault. This method has defined metrics of sample distribution in feature space, which are used as the rule of constructing hierarchical SVMs. Experiment results show that this method can restrain error accumulation and has higher multiclass classification accuracy, and offer an effective way for network fault diagnosis.

Keywords

Internet; computer network reliability; fault diagnosis; learning (artificial intelligence); support vector machines; telecommunication computing; Internet; error accumulation; feature space; hierarchical SVM; kernel method; multiclass classification accuracy; network fault diagnosis; sample distribution; support vector machine; Artificial intelligence; Data engineering; Data mining; Extraterrestrial measurements; Fault diagnosis; Kernel; Knowledge engineering; Machine learning; Support vector machine classification; Support vector machines; Hierarchical SVMs; Kernel Method; Network Fault Diagnosis;

fLanguage

English

Publisher

ieee

Conference_Titel

Knowledge Discovery and Data Mining, 2009. WKDD 2009. Second International Workshop on

Conference_Location

Moscow

Print_ISBN

978-0-7695-3543-2

Type

conf

DOI

10.1109/WKDD.2009.79

Filename

4772045