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Joint Feature Transformation and Selection Based on Dempster-Shafer Theory

  • Chunfeng LianEmail author
  • Su Ruan
  • Thierry Denœux
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 610)

Abstract

In statistical pattern recognition, feature transformation attempts to change original feature space to a low-dimensional subspace, in which new created features are discriminative and non-redundant, thus improving the predictive power and generalization ability of subsequent classification models. Traditional transformation methods are not designed specifically for tackling data containing unreliable and noisy input features. To deal with these inputs, a new approach based on Dempster-Shafer Theory is proposed in this paper. A specific loss function is constructed to learn the transformation matrix, in which a sparsity term is included to realize joint feature selection during transformation, so as to limit the influence of unreliable input features on the output low-dimensional subspace. The proposed method has been evaluated by several synthetic and real datasets, showing good performance.

Keywords

Belief functions Dempster-Shafer theory Feature transformation Feature selection Pattern classification 

Notes

Acknowledgements

This work was partly supported by China Scholarship Council.

References

  1. 1.
    Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci. 2(1), 183–202 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Denœux, T.: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25(5), 804–813 (1995)CrossRefGoogle Scholar
  3. 3.
    Denœux, T., Kanjanatarakul, O., Sriboonchitta, S.: EK-NNclus: a clustering procedure based on the evidential k-nearest neighbor rule. Knowl.-Based Syst. 88, 57–69 (2015)CrossRefGoogle Scholar
  4. 4.
    Denœux, T., Smets, P.: Classification using belief functions: relationship between case-based and model-based approaches. IEEE Trans. Syst. Man Cybern. Part B Cybern. 36(6), 1395–1406 (2006)CrossRefGoogle Scholar
  5. 5.
    Goldberger, J., Roweis, S., Hinton, G., Salakhutdinov, R.: Neighbourhood components analysis. In: Advances in Neural Information Processing Systems, pp. 513–520 (2005)Google Scholar
  6. 6.
    Jiao, L., Pan, Q., Denoeux, T., Liang, Y., Feng, X.: Belief rule-based classification system: extension of FRBCS in belief functions framework. Inf. Sci. 309, 26–49 (2015)CrossRefGoogle Scholar
  7. 7.
    Lelandais, B., Ruan, S., Denœux, T., Vera, P., Gardin, I.: Fusion of multi-tracer PET images for dose painting. Med. Image Anal. 18(7), 1247–1259 (2014)CrossRefGoogle Scholar
  8. 8.
    Lian, C., Ruan, S., Denœux, T.: An evidential classifier based on feature selection and two-step classification strategy. Pattern Recogn. 48(7), 2318–2327 (2015)CrossRefGoogle Scholar
  9. 9.
    Lian, C., Ruan, S., Dencœux, T., Li, H., Vera, P.: Dempster-Shafer theory based feature selection with sparse constraint for outcome prediction in cancer therapy. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 695–702. Springer, Heidelberg (2015)Google Scholar
  10. 10.
    Liu, Z., Pan, Q., Mercier, G., Dezert, J.: A new incomplete pattern classification method based on evidential reasoning. IEEE Trans. Cybern. 45(4), 635–646 (2015)CrossRefGoogle Scholar
  11. 11.
    Ma, L., Destercke, S., Wang, Y.: Online active learning of decision trees with evidential data. Pattern Recogn. 52, 33–45 (2016)CrossRefGoogle Scholar
  12. 12.
    Makni, N., Betrouni, N., Colot, O.: Introducing spatial neighbourhood in evidential C-means for segmentation of multi-source images: application to prostate multi-parametric MRI. Inf. Fusion 19, 61–72 (2014)CrossRefGoogle Scholar
  13. 13.
    Masson, M.H., Denœux, T.: ECM: an evidential version of the fuzzy C-means algorithm. Pattern Recogn. 41(4), 1384–1397 (2008)CrossRefzbMATHGoogle Scholar
  14. 14.
    Nguyen, T., Boukezzoula, R., Coquin, D., Perrin, S.: Combination of sugeno fuzzy system and evidence theory for NAO robot in colors recognition. In: 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–8 (2015)Google Scholar
  15. 15.
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)zbMATHGoogle Scholar
  16. 16.
    Smets, P., Kennes, R.: The transferable belief model. Artif. Intell. 66(2), 191–234 (1994)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Wang, F., Miron, A., Ainouz, S., Bensrhair, A.: Post-aggregation stereo matching method using Dempster-Shafer theory. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 3783–3787 (2014)Google Scholar
  18. 18.
    Weinberger, K.Q., Saul, L.K.: Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res. 10, 207–244 (2009)zbMATHGoogle Scholar
  19. 19.
    Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M.: Use of the zero norm with linear models and kernel methods. J. Mach. Learn. Res. 3, 1439–1461 (2003)MathSciNetzbMATHGoogle Scholar
  20. 20.
    Zhou, K., Martin, A., Pan, Q., Liu, Z.-G.: Median evidential C-means algorithm and its application to community detection. Knowl.-Based Syst. 74, 69–88 (2015)CrossRefGoogle Scholar
  21. 21.
    Zouhal, L.M., Denœux, T.: An evidence-theoretic K-NN rule with parameter optimization. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 28(2), 263–271 (1998)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Sorbonne Universités, Université de Technologie de Compiègne, CNRSCompiègneFrance
  2. 2.Université de RouenRouenFrance

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