JOURNAL ARTICLE

Network fault diagnosis based on rough set-support vector machine

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:
Support vector machine Rough set Structured support vector machine Computer science Relevance vector machine Artificial intelligence Set (abstract data type) Fault (geology) Data mining Machine learning Domain (mathematical analysis) Vector space Pattern recognition (psychology) Mathematics

Metrics

1
Cited By
0.00
FWCI (Field Weighted Citation Impact)
5
Refs
0.07
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Computational Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Rough Sets and Fuzzy Logic
Physical Sciences →  Computer Science →  Computational Theory and Mathematics
Elevator Systems and Control
Physical Sciences →  Engineering →  Control and Systems Engineering
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