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Generate two-dimensional belief function based on an improved similarity measure of trapezoidal fuzzy numbers

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Abstract

Dempster–Shafer evidence theory plays a significant role in addressing uncertain information in various data fusion application systems. Recently, a new framework to model uncertain and partially reliable information on the basis of Dempster–Shafer evidence theory is put forward, called two-dimensional belief function (TDBF). A TDBF consists of two classical belief functions, \(T=(m_A,m_B)\), where \(m_B\) is a measure of reliability of \(m_A\). In this paper, an approach for determining TDBF is presented based on the improved similarity measure of fuzzy numbers. The improved similarity measure is more logical, flexible and can obviously improve the effectiveness in classification problem. Compared to the classical belief function, the TDBF can achieve better classification effective. The processes of the determine approach are expounded through a classification problem of Iris data. The validity of the determine approach is further illustrated by the classification of Wheat data.

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Acknowledgements

The work is partially supported by National Natural Science Foundation of China (Grant no. 61973332).

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Authors and Affiliations

  1. Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China, Chengdu, 610054, China

    Yangxue Li & Yong Deng

  2. Faculty of Communications Sciences, University of Teramo, Teramo, Italy

    Danilo Pelusi

  3. School of Education, Shannxi Normal University, Xi’an, 710062, China

    Yong Deng

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  1. Yangxue Li
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  2. Danilo Pelusi
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Correspondence to Yong Deng.

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Li, Y., Pelusi, D. & Deng, Y. Generate two-dimensional belief function based on an improved similarity measure of trapezoidal fuzzy numbers. Comp. Appl. Math. 39, 326 (2020). https://doi.org/10.1007/s40314-020-01371-9

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  • DOI: https://doi.org/10.1007/s40314-020-01371-9

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