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Abstract: Multi-source multi-class classification methods based on multi-class Support Vector Machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class Support Vector Machine classifier is trained. In the distributed schemes, the individual data sources are processed separately and modelled by using the multi-class Support Vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class Support Vector Machine models. Our proposed fusion strategies take into account that an Support Vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

[1] Benediktsson, J.A., Sveinsson, J.R., Ersoy, O.K., Swain, P.H., 1997. Parallel consensual neural networks. IEEE Transactions on Neural Networks, 8(1):54-64.

[2] Burges, C.J.C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2):121-167.

[3] Byington, C.S., Garga, A.K., 2001. Data Fusion for Developing Predictive Diagnostics for Electromechanical Systems. In: Hall, D.L., Llinas, J. (Eds.), Handbook of Multisensor Data Fusion. CRC Press, Boca Raton, FL, p.1-32.

[4] Cristianini, N., Shawe-Taylor, J., 2000. An Introduction to Support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, Cambridge.

[5] Duda, R.O., Hart, P.E., Stork, D.G., 2001. Pattern Classification (2nd Edition). John Wiley & Sons, Inc., New York, p.483.

[6] Friedman, J., 1996. Another Approach to Polychotomous Classification. Technical Report, Department of Statistics, Stanford University, Stanford, CA.

[7] Grox, X.E., 1997. NDT Data Fusion. John Wiley & Sons, Inc., New York, p.1-42.

[8] Hajiaghajani, M., Toliyat, H.A., Panahi, I.M.S., 2004. Advanced fault diagnosis of a DC motor. IEEE Transactions on Energy Conversion, 19(1):60-65.

[9] Hall, D.L., Llinas, J., 1997. An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1):6-23.

[10] Hsu, C.W., Lin, C.J., 2002. A comparison of methods for multi-class Support Vector Machines. IEEE Transactions on Neural Networks, 13(2):415-425.

[11] Liggins, M.E. II, Chong, C.Y., Kadar, I., Alford, M.G., Vannicola, V., Thomopoulos, S., 1997. Distributed Fusion architectures and algorithms for target tracking. Proceedings of the IEEE, 85(1):95-107.

[12] Pan, H., Liang, Z.P., Anastasio, T.J., Huang, T.S., 1998. A Hybrid NN-bayesian Architecture for Information Fusion. Image Processing, ICIP 98, 1:368-371.

[13] Papastavrou, J.D., Athans, M., 1992. On optimal distributed decision architectures in a hypothesis testing environment. IEEE Transactions on Automatic Control, 37(8):1154-1169.

[14] Platt, J.C., Cristianini, N., Shawe-Taylor, J., 2000. Large Margin DAG’s for Multiclass Classification. In: Solla, S.A., Leen, T.K., Müller, K.R. (Eds.), Advances in Neural Information Processing Systems. MIT Press, Cambridge, MA, 12:547-553.

[15] Shen, L., Tay, F.E.H., Qu, L., Shen, Y., 2000. Fault diagnosis using rough sets theory. Computers in Industry, 43:61-72.

[16] Tay, F.E.H., Shen, L., 2003. Fault diagnosis based on rough set theory. Engineering Application of Artificial Intelligence, 16:39-43.

[17] Thomopoulos, S.C.A., Viswanathan, R., Bougoulias, D.K., 1989. Optimal distributed decision fusion. IEEE Transactions on Aerospace and Electronic Systems, 25(5):761-765.

[18] Vapnik, V.N., 1998. Statistical Learning Theory. Wiley, New York.

[19] Yan, W., Shao, H., Wang, X., 2003. Parallel Decision Models Based on Support Vector Machines and Their Application to Distributed Fault Diagnosis. Proceedings of the American Control Conference, 2:1770-1775.