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Decision Fusion Using Fuzzy Dempster-Shafer Theory

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Recent Advances in Information and Communication Technology 2018 (IC2IT 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 769))

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Abstract

One of the popular tools in decision making is a decision fusion since there might be several sources that provide decisions for one task. The Dempster’s rule of combination is one of the decision fusion methods used frequently in many research areas. However, there are so many uncertainties in classifier output. Hence, we propose a fuzzy Dempster’s rule of combination (FDST) where we fuzzify the discounted basic probability assignment and compute the fuzzy combination. We also have a rejection criterion for any sample with higher belief in both classes, not only one of the classes. We run the experiment with 2 classifiers, i.e., support vector machine (SVM) and radial basis function (RBF). We test our algorithm on 5 data sets from the UCI machine learning repository and SAR images on three military vehicle types. We compare our fusion result with that from the regular Dempster’s rule of combination (DST). All of our results are comparable or better than those from the DST.

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References

  1. Li, W., Leung, H., Kwan, C., Linnell, B.R.: E-nose vapor identification based on Dempster-Shafer fusion of multiple classifiers. IEEE Trans. Instrum. Measur. 57(10), 2273–2282 (2008)

    Article  Google Scholar 

  2. Mejdoubi, M., Aboutajdine, D., Kerroum, M.A., Hammouch, A.: Combining classifiers using Dempster-Shafer evidence theory to improve remote sensing images classification. In: International Conference on Multimedia Computing and Systems. IEEE, Ouarzazate (2011)

    Google Scholar 

  3. Verma, P., Yadava, R.D.S.: Evidence generation for Dempster-Shafer fusion using feature extraction multiplicity and radial basis network. In: International Conference on Emerging Trends in Electrical and Computer Technology, Chunkankadai, India, pp. 542–545 (2011)

    Google Scholar 

  4. Mohandes, M., Deriche, M.: Arabic sign language recognition by decisions fusion using Dempster-Shafer theory of evidence. In: Computing, Communications and IT Applications Conference (ComComAp), pp. 90–94. IEEE, Hong Kong (2013)

    Google Scholar 

  5. Bagheri, M.A., Hu, G., Gao, Q., Escalera, S.: A framework of multi-classifier fusion for human action recognition. In: International Conference on Pattern Recognition, pp. 1260–1265. IEEE (2014)

    Google Scholar 

  6. Perez, A., Tabia, H., Declercq, D., Zanotti, A.: Using the conflict in Dempster-Shafer evidence theory as a rejection criterion in classifier output combination for 3D human action recognition. Image Vis. Comput. 55(2), 149–157 (2016)

    Article  Google Scholar 

  7. Ladjal, M., Bouamar, M., Djerioui, M., Brik, Y.: Performance evaluation of ANN and SVM multiclass models for intelligent water quality classification using Dempster-Shafer theory. In: 2nd International Conference on Electrical and Information Technologies, Movenpick Hotel Tangier Tangier, Morocco, pp. 191–196 (2016)

    Google Scholar 

  8. Ming, Y., You, L., Xueshan, H.: Probabilistic wind generation forecast based on sparse Bayesian classification and Dempster-Shafer theory. IEEE Trans. Ind. Appl. 52(3), 1998–2005 (2016)

    Article  Google Scholar 

  9. Saha, S., Saha, S.: Combined committee machine for classifying dengue fever. In: International Conference on Microelectronics, Computing and Communication. IEEE, Durgapur (2016)

    Google Scholar 

  10. Liu, Z., Pan, Q., Dezert, J., Han, J., He, Y.: Classifier fusion with contextual reliability evaluation. IEEE Trans. Cybern. 99, 1–14 (2017)

    Article  Google Scholar 

  11. Li, L., Tang, J., Liu, Y.: Partial discharge recognition in gas insulated switchgear based on multi-information fusion. IEEE Trans. Dielectr. Electr. Insul. 22(2), 1080–1087 (2015)

    Article  Google Scholar 

  12. Jiao, L., Denoux, T., Pan, Q.: Fusion of pairwise nearest-neighbor classifiers based on pairwise-weighted distance metric and Dempster-Shafer theory. In: 17th International Conference on Information Fusion. IEEE, Salamanca (2014)

    Google Scholar 

  13. Yen, J.: Generalizing the Dempster-Shafer theory to fuzzy sets. IEEE Tran. Syst. Man Cybern. 20(3), 559–570 (1990)

    Article  MathSciNet  Google Scholar 

  14. Binaghi, E., Madella, P.: Fuzzy Dempster-Shafer reasoning for rule-based classifiers. Int. J. Intell. Syst. 14, 559–583 (1999)

    Article  Google Scholar 

  15. Zhu, H., Basir, O.: A K-NN associated fuzzy evidential reasoning classifier with adaptive neighbor selection. In: Third IEEE International Conference on Data Mining, pp. 709–712 (2003)

    Google Scholar 

  16. Dutta, P., Ali, T.: Fuzzy focal elements in Dempster-Shafer theory of evidence: case study in risk analysis. Int. J. Comput. Appl. 34(1), 46–53 (2011)

    Google Scholar 

  17. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Article  MathSciNet  Google Scholar 

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

    MATH  Google Scholar 

  19. Klir, G., Yuan, B.: Fuzzy Sets and Fuzzy Logic: Theory and Application. Prentice Hall, New Jersey (1995)

    MATH  Google Scholar 

  20. Zadeh, L.: Outline of new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. 3(1), 28–44 (1973)

    Article  MathSciNet  Google Scholar 

  21. Dong, W., Shah, H., Wong, F.: Fuzzy computations in risk and decision analysis. Civ. Eng Syst. 2, 201–208 (1985)

    Article  Google Scholar 

  22. Dong, W., Wong, F.: Fuzzy weighted averages and implementation of the extension principle. Fuzzy Sets Syst. 21, 183–199 (1987)

    Article  MathSciNet  Google Scholar 

  23. Mizumoto, M., Tanaka, K.: Some properties of fuzzy numbers in advances in fuzzy sets theory and applications. In: Gupta, M.M. (ed.) Advances in Fuzzy Set Theory and Applications, pp. 153–164. North-Holland, Amsterdam (1979)

    Google Scholar 

  24. Moore, R.: Interval Analysis. Prentice-Hall, New Jersey (1966)

    MATH  Google Scholar 

  25. Ludmila, K.: Combining Pattern Classifiers Methods and Algorithms, 2nd edn. Wiley, Hoboken (2014)

    MATH  Google Scholar 

  26. Haykin, S.S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Upper Saddle River (1999)

    MATH  Google Scholar 

  27. Shafer, G.: Perspectives on the theory and practice of belief functions. Int. J. Approx. Reason. 4(5–6), 323–362 (1990)

    Article  MathSciNet  Google Scholar 

  28. Choobineh, F., Li, H.: An index for ordering fuzzy numbers. Fuzzy Sets Syst. 54(3), 287–294 (1993)

    Article  MathSciNet  Google Scholar 

  29. Blake, C., Keogh, E., Merz, C.J.: UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA (1998). http://www.ics.uci.edu/~mlearn/MLRepository.html

  30. Auephanwiriyakul, S., Munklang, Y., Theera-Umpon, N.: Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) using fuzzy co-occurrence matrix texture features. In: Abielmona, R., Falcon, R., Zincir-Heywood, N., Abbass, H. (eds.) Recent Advances in Computational Intelligence in Defense and Security. Studies in Computational Intelligence, vol. 621. Springer, Cham (2016)

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

    Somnuek Surathong & Sansanee Auephanwiriyakul

  2. Graduate School, Chiang Mai University, Chiang Mai, Thailand

    Somnuek Surathong

  3. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand

    Nipon Theera-Umpon

  4. Biomedical Engineering Institute, Chiang Mai University, Chiang Mai, Thailand

    Sansanee Auephanwiriyakul & Nipon Theera-Umpon

Authors
  1. Somnuek Surathong
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  2. Sansanee Auephanwiriyakul
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  3. Nipon Theera-Umpon
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Corresponding author

Correspondence to Sansanee Auephanwiriyakul .

Editor information

Editors and Affiliations

  1. LG Kommunikationsnetze, Fern Universität in Hagen, Hagen, Germany

    Herwig Unger

  2. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Sunantha Sodsee

  3. Faculty of Information Technology, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

    Phayung Meesad

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Surathong, S., Auephanwiriyakul, S., Theera-Umpon, N. (2019). Decision Fusion Using Fuzzy Dempster-Shafer Theory. In: Unger, H., Sodsee, S., Meesad, P. (eds) Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-93692-5_12

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