Some novel distance measures between dual hesitant fuzzy sets and their application in medical diagnosis
Some novel distance measures between dual hesitant fuzzy sets and their application in medical diagnosis
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DOI：
10.1002/int.22960
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发表时间：
2022-07
影响因子：
7
通讯作者：
Wenyi Zeng;Rong Ma;Zeping Liu;Yue Xi;Qian Yin;Zeshui Xu
中科院分区：
文献类型：
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作者：
Wenyi Zeng;Rong Ma;Zeping Liu;Yue Xi;Qian Yin;Zeshui Xu
对偶犹豫模糊集(DHFS)利用隶属度和非隶属度来描述现实世界中的不确定性。它能够全面地收集模糊信息，并将其有效地应用于决策任务。本文提取了平均函数、方差函数、迟疑度等特征来描述双迟疑模糊元，并基于这些特征提出了一种新的距离度量方法。进一步，我们研究了它们的性质，并证明了距离度量的三角不等式。最后，我们将其应用于实际的医疗诊断中，以说明我们提出的距离度量的有效性。
A dual hesitant fuzzy set (DHFS) describes the uncertainty in the real world by using the membership degree and nonmembership degree. It can collect fuzzy information comprehensively and apply them into decision‐making tasks efficiently. In this article, we extract some characteristics, such as the average function, variance function, hesitancy degree to describe a dual hesitant fuzzy element, and develop novel distance measures of DHFSs based on these characteristics. Further, we investigate their properties and prove the triangle inequality of distance measure. Finally, we apply it in practical medical diagnosis to illustrate the validity of our proposed distance measures.