Network Fault Diagnosis of SVM Based on Numerical Attribute Reduction
Zhuang Ling-yi · Jisuanji gongcheng · 2009
Correlative transmitting of network faults may bring lots of redundancy information included in the network fault datas, which will affect the precision and efficiency of diagnosis.According to the characteristic of fault datas, numerical attribute reduction algorithms based on neighborhood rough approximation are adopted to carry out fast and highly efficient faults diagnosis by uniting rough set with Support Vector Machine(SVM).The discrete error in the classical RS is conquered, the memory space of data is curtailed, the complexity of SVM training model is reduced greatly, and the speed of training is put up.The well generalization of this method is analyzed and validated by ROC performance curve.