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Multiple Sources Data Fusion Strategies Based on Multi-class Support Vector Machine

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Advances in Neural Networks - ISNN 2008 (ISNN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5263))

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Abstract

Data fusion from multiple sources is very important and useful. Some strategies for multiple sources data fusion based on Multi-class Support Vector Machine (MSVM) are proposed in this paper. The features (independent components) of data from multiple sources are extracted for fusion. The Dempster-Shafer theory (DS theory) and Bayesian theory are used to combine the probabilistic outputs of MSVMs. Then the outputs of DS theory combination are classified by a MSVM to get the final decision of the classification. Finally, these strategies are evaluated by three data sets and the results show that DS theory can improve the accuracy obviously, and the strategies based on MSVM and DS theory is very fit for solving problems with small data sets.

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References

  1. Hall, D.L., Llinas, J.: An Introduction to Multisensor Data Fusion. In: Proc. IEEE, vol. 85, pp. 6–23 (1997)

    Google Scholar 

  2. Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    MATH  Google Scholar 

  3. Song, H.Z., Zhong, L., Han, B.: Structural Damage Detection by Integrating Independent Component Analysis and Support Vector Machine. Int. J. Systems Science 37, 961–967 (2006)

    Article  MATH  Google Scholar 

  4. Hsu, C., Lin, C.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Trans. Neural Networks 13, 415–425 (2002)

    Article  Google Scholar 

  5. Platt, J.: Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Methods. In: Advances in Large Margin Classifiers, MIT Press, Cambridge (2000)

    Google Scholar 

  6. Wu, T.F., Lin, C.J., Weng, R.C.: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research 5, 975–1005 (2004)

    MathSciNet  Google Scholar 

  7. Hoffman, J.C., Murphy, R.R.: Comparison of Bayesian and Dempster-Shafer Theory for Sensing: A Practitioner’s Approach. In: Proc. SPIE, vol. 203, pp. 255–279 (1993)

    Google Scholar 

  8. Shafer, G.: A Mathematical Theory of Evidence. Princeton Press, Princeton (1976)

    MATH  Google Scholar 

  9. Murphy, R.R.: Dempster-Shafer Theory for Sensor Fusion in Autonomous Mobile Robots. IEEE Trans. Robot. Automat. 14, 197–206 (1998)

    Article  Google Scholar 

  10. Tang, J.Y., Shi, Y.B., Zhang, W.: Analogue Electronic Circuit Fault Diagnosis Based on Hierarchical Support Vector Machine and Dempster-Shafer Theory. In: 8th International Conference on Electronic Measurement and Instruments, pp. 565–570 (2007)

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, Hubei, P.R.C, 430070

    Luo Zhong, Zhe Li, Zichun Ding, Cuicui Guo & Huazhu Song

Authors
  1. Luo Zhong
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  2. Zhe Li
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  3. Zichun Ding
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  4. Cuicui Guo
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  5. Huazhu Song
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghu University, 100084, Beijing, China

    Fuchun Sun

  2. Institute TAMS (Technical Aspects of Multimodal Systems), department of Informatics, University of Hamburg, Vogt-Koelln-Straße 30, 22527, Hamburg, Germany

    Jianwei Zhang

  3. Intel China Research Center, 8/F, Peking University, Department of Machine Intelligence, 100871, Beijing, China

    Ying Tan

  4. Department of Mathematics, Southeast University, 210096, Nanjing, China

    Jinde Cao

  5. Departamento de Control Automático, CINVESTAV-IPN, A.P. 14-740, Av.IPN 2508, 07360, México D.F., México

    Wen Yu

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© 2008 Springer-Verlag Berlin Heidelberg

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Zhong, L., Li, Z., Ding, Z., Guo, C., Song, H. (2008). Multiple Sources Data Fusion Strategies Based on Multi-class Support Vector Machine. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_80

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  • DOI: https://doi.org/10.1007/978-3-540-87732-5_80

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87731-8

  • Online ISBN: 978-3-540-87732-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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