Determining Basic Probability Assignment Based on the Improved Similarity Measures of Generalized Fuzzy Numbers

Wen Jiang, Yan Yang, Yu Luo, Xiyun Qin


Dempster-Shafer theory of evidence has been widely used in many data fusion application systems. However, how to determine basic probability assignment, which is the main and the first step in evidence theory, is still an open issue. In this paper, an improved method to determine the similarity measure between generalized fuzzy numbers is presented. The proposed method can overcome the drawbacks of the existing similarity measures. Then, we propose a new method for obtaining basic probability assignment (BPA) based on the proposed similarity measure method between generalized fuzzy numbers. Finally, the efficiency of the proposed method is illustrated by the classification of Iris data.


data fusion, dempster-Shafer evidence theory, basic probability assignment (BPA), generalized fuzzy numbers, similarity measures

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