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Support Vector Machines Ensemble Based on Fuzzy Integral for Classification

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

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Abstract

Support vector machines (SVMs) ensemble has been proposed to improve classification performance recently. However, currently used fusion strategies do not evaluate the importance degree of the output of individual component SVM classifier when combining the component predictions to the final decision. A SVMs ensemble method based on fuzzy integral is presented in this paper to deal with this problem. This method aggregates the outputs of separate component SVMs with importance of each component SVM, which is subjectively assigned as the nature of fuzzy logic. The simulating results demonstrate that the proposed method outperforms a single SVM and traditional SVMs aggregation technique via majority voting in terms of classification accuracy.

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References

  1. Kim, H.C., Pang, S., Je, H.M., Kim, D., Bang, S.Y.: Constructing Support Vector Machine Ensemble. Pattern Recognition 36, 2757–2767 (2003)

    Article  MATH  Google Scholar 

  2. Anna, K.J., James, D.M., Marek, F., Ronald, M.S.: Computer-aided Polyp Detection in CT Colonography Using an Ensemble of Support Vector Machines. International Congress Series 1256, 1019–1024 (2003)

    Article  Google Scholar 

  3. Valentini, G., Muselli, M., Ruffino, F.: Cancer Recognition with Bagged Ensemble of Support Vector Machines. Neurocomputing 56, 461–466 (2004)

    Article  Google Scholar 

  4. Vapnik, V.: The Nature of Statistical Learning Theory. Wiley, New York (1998)

    Google Scholar 

  5. Platt, J.: Probabilistic Outputs for Support Vector Machines and Comparison to Regularized Likelihood Method. In: Smola, A., Bartlett, P., Schuurmans, D. (eds.) Advance in Large Margin Classifiers. MIT Press, Cambridge (2000)

    Google Scholar 

  6. Hsu, C.W., Lin, C.J.: A Comparison of Method for Mutli-class Support Vector Machines. IEEE Trans. Neural Networks 13(2), 415–425 (2002)

    Article  Google Scholar 

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

    MathSciNet  Google Scholar 

  8. Breiman, L.: Bagging Predictors. Machine Learning 24(2), 123–140 (1996)

    MATH  MathSciNet  Google Scholar 

  9. Kwak, K.C., Pedrycz, W.: Face Recognition: A Study in Information Fusion Using Fuzzy Integral. Pattern Recognition Letters 26, 719–733 (2005)

    Article  Google Scholar 

  10. Chang, C.C., Lin, C.J.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

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Author information

Authors and Affiliations

  1. Department of Control Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, P.R. China

    Genting Yan, Guangfu Ma & Liangkuan Zhu

Authors
  1. Genting Yan
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  2. Guangfu Ma
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  3. Liangkuan Zhu
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Yan, G., Ma, G., Zhu, L. (2006). Support Vector Machines Ensemble Based on Fuzzy Integral for Classification. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_143

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  • DOI: https://doi.org/10.1007/11759966_143

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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