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Title: $A Fault Diagnosis Method Based on Ensemble Support Vector Machines
Abstract: In order to enhance the generalization ability of Support Vector Machine(SVM),Bagging ensemble learning algorithm was studied.The experimental results of Bagging SVM in the standard data set showed that the Bagging method couldn't enhance the generalization ability of SVM markedly.In order to find reason of this,the stability of SVM and neural network was studied.The results showed that SVM is a relative stable classifier in comparison with neural network.Then,a double disturbance algorithm was proposed,in which the subspace method was used for data characteristics disturbance,and Bagging method for data distribution disturbance.Experiments were made by using double disturbance algorithm for the standard data sets and fault diagnosis data set,and the results showed that the recognition rate of SVM is obviously enhanced by this method.