Dempster-shafer Theory Based Multi-class Support Vector Machines and Their Applications

نویسندگان

  • Zhonghui Hu
  • Rupo Yin
  • Yuangui Li
  • Xiaoming Xu
چکیده

How to extend standard support vector machines to solve multi-class classification problem and yield the outputs in the frame of Dempster-Shafer theory is useful. The multi-class probability support vector machine is proposed, firstly. The Dempster-Shafer theory based multi-class support vector machine is designed by constructing probability support vector machines for binary classification using one-against-all strategy and then combining them using Dempster-Shafer theory. Our proposed method is applied to fault diagnosis for a diesel engine. The experimental results show our proposed method obtains a comparable performance with that of standard multi-class support vector machines. Furthermore, the uncertainty can also be evaluated. Copyright © 2005 IFAC

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تاریخ انتشار 2005