GERTIS: a Dempster-Shafer approach to diagnosing hierarchical hypotheses
Communications of the ACM
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Fault diagnosis using Rough Sets Theory
Computers in Industry
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Evidence Theory and Its Applications
Evidence Theory and Its Applications
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)
Parallel consensual neural networks
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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The multi-class probability SVM (MPSVM) is designed by training the sigmoid function to map the output of each binary class SVM into a posterior probability, and then combining these learned binary-class PSVMs using one-against-all strategy. The method of basic probability assignment is proposed according to the probabilistic output and performance of the PSVM. The outputs of all the binary-class PSVMs comprising an MPSVM are represented in the frame of Dempster-Shafer theory. A Dempster-Shafer theory based multi-class SVM (DSMSVM) is constructed by using the combination rule of evidences. To deal with the distributed multi-source multi-class problem, the DSMSVM is trained corresponding to each information source, and then the Dempster-Shafer theory is used to combine these learned DSMSVMs. Our proposed method is applied to fault diagnosis of a diesel engine. The experimental results show that the accuracy and robustness of fault diagnosis can be improved by using our proposed approach.