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An improved method to determine basic probability assignment with interval number and its application in classification

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  • Bowen Qin
  • Fuyuan Xiao

Abstract

Due to its efficiency to handle uncertain information, Dempster–Shafer evidence theory has become the most important tool in many information fusion systems. However, how to determine basic probability assignment, which is the first step in evidence theory, is still an open issue. In this article, a new method integrating interval number theory and k -means++ cluster method is proposed to determine basic probability assignment. At first, k -means++ clustering method is used to calculate lower and upper bound values of interval number with training data. Then, the differentiation degree based on distance and similarity of interval number between the test sample and constructed models are defined to generate basic probability assignment. Finally, Dempster’s combination rule is used to combine multiple basic probability assignments to get the final basic probability assignment. The experiments on Iris data set that is widely used in classification problem illustrated that the proposed method is effective in determining basic probability assignment and classification problem, and the proposed method shows more accurate results in which the classification accuracy reaches 96.7%.

Suggested Citation

  • Bowen Qin & Fuyuan Xiao, 2019. "An improved method to determine basic probability assignment with interval number and its application in classification," International Journal of Distributed Sensor Networks, , vol. 15(1), pages 15501477188, January.
    • Handle: RePEc:sae:intdis:v:15:y:2019:i:1:p:1550147718820524
    DOI: 10.1177/1550147718820524
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    References listed on IDEAS

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    1. Jiang, C. & Han, X. & Liu, G.R. & Liu, G.P., 2008. "A nonlinear interval number programming method for uncertain optimization problems," European Journal of Operational Research, Elsevier, vol. 188(1), pages 1-13, July.
    2. Wen Jiang & Boya Wei, 2018. "Intuitionistic fuzzy evidential power aggregation operator and its application in multiple criteria decision-making," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(3), pages 582-594, February.
    3. Frikha, Ahmed & Moalla, Hela, 2015. "Analytic hierarchy process for multi-sensor data fusion based on belief function theory," European Journal of Operational Research, Elsevier, vol. 241(1), pages 133-147.
    4. Kang, Bingyi & Chhipi-Shrestha, Gyan & Deng, Yong & Hewage, Kasun & Sadiq, Rehan, 2018. "Stable strategies analysis based on the utility of Z-number in the evolutionary games," Applied Mathematics and Computation, Elsevier, vol. 324(C), pages 202-217.
    5. Beynon, Malcolm & Curry, Bruce & Morgan, Peter, 2000. "The Dempster-Shafer theory of evidence: an alternative approach to multicriteria decision modelling," Omega, Elsevier, vol. 28(1), pages 37-50, February.
    6. Wen Jiang & Boya Wei & Xiyun Qin & Jun Zhan & Yongchuan Tang, 2016. "Sensor Data Fusion Based on a New Conflict Measure," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-11, August.
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    Cited by:

    1. Liguo Fei & Jun Xia & Yuqiang Feng & Luning Liu, 2019. "A novel method to determine basic probability assignment in Dempster–Shafer theory and its application in multi-sensor information fusion," International Journal of Distributed Sensor Networks, , vol. 15(7), pages 15501477198, July.

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