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- W2377135577 abstract "The multi-class classification algorithms based on traditional Support Vector Machine(SVM) are weak on training speed when dealing with large-scale data.To solve the problem,this paper proposed a multi-class classification algorithm based on Twin Support Vector Machine(TWSVM).It combined binary tree multi-class classification and constructed classifiers based on TWSVM on the nodes of binary tree.To reduce the error accumulation of Binary Tree SVM(BT-SVM),it firstly got clustering centers through the clustering algorithm,and then compared the distances between them to determine the separation sequence of classes.Finally,it was applied to network intrusion detection.The experimental results show that the proposed algorithm has higher detection accuracy and certain advantages on training speed especially for large-scale data.The training speed is approximately two times faster than that of the traditional BT-SVM algorithm.It is valuable for large-scale data processing in the field of network intrusion detection." @default.
- W2377135577 created "2016-06-24" @default.
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- W2377135577 date "2013-01-01" @default.
- W2377135577 modified "2023-09-24" @default.
- W2377135577 title "Application of multi-class classification algorithm based on twin support vector machine in intrusion detection" @default.
- W2377135577 hasPublicationYear "2013" @default.
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