Abstract
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.
Supported by the National Key Fundamental Research Program (2002cb312200), and partially supported by the National High Technology Research and Development Program (2002AA412010), and the Natural Science Foundation of China (60174038).
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Hu, Z., Cai, Y., Li, Y., Li, Y., Xu, X. (2005). Data Fusion for Fault Diagnosis Using Dempster-Shafer Theory Based Multi-class SVMs. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_27
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DOI: https://doi.org/10.1007/11539117_27
Publisher Name: Springer, Berlin, Heidelberg
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