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Novel similarity measures in spherical fuzzy environment and their applications.
Engineering Applications of Artificial Intelligence ( IF 8 ) Pub Date : 2020-07-28 , DOI: 10.1016/j.engappai.2020.103837
Seyed Amin Seyfi Shishavan 1 , Fatma Kutlu Gündoğdu 2 , Elmira Farrokhizadeh 1 , Yaser Donyatalab 1 , Cengiz Kahraman 1
Affiliation  

Spherical fuzzy sets (SFSs) have gained great attention from researchers in various fields. The spherical fuzzy set is characterized by three membership functions expressing the degrees of membership, non-membership and the indeterminacy to provide a larger preference domain. It was proposed as a generalization of picture fuzzy sets and Pythagorean fuzzy sets in order to deal with uncertainty and vagueness information. The similarity measure is one of the essential and advantageous tools to determine the degree of similarity between items. Several studies on similarity measures have been developed due to the importance of similarity measure and application in decision making, data mining, medical diagnosis, and pattern recognition in the literature. The contribution of this study is to present some novel spherical fuzzy similarity measures. We develop the Jaccard, exponential, and square root cosine similarity measures under spherical fuzzy environment. Each of these similarity measures is analyzed with respect to decision-makers’ optimistic or pessimistic point of views. Then, we apply these similarity measures to medical diagnose and green supplier selection problems. These similarity measures can be computed easily and they can express the dependability similarity relation apparently.



中文翻译:

球形模糊环境中的新型相似性度量及其应用。

球形模糊集(SFS)受到了各个领域研究人员的极大关注。球形模糊集的特征在于三个隶属度函数,表示隶属度,非隶属度和不确定性以提供更大的偏好域。为了处理不确定性和模糊性信息,提出将其作为图片模糊集和勾股模糊集的推广。相似性度量是确定项目之间相似度的必要且有利的工具之一。由于相似性度量的重要性及其在决策,数据挖掘,医学诊断和模式识别中的应用,因此已经开展了一些关于相似性度量的研究。这项研究的贡献是提出了一些新颖的球形模糊相似性度量。我们开发了球形模糊环境下的Jaccard,指数和平方根余弦相似度度量。针对决策者的乐观或悲观观点,分析了每种相似性度量。然后,我们将这些相似性度量应用于医学诊断和绿色供应商选择问题。这些相似性度量可以轻松计算,并且可以明显地表达可靠性相似性关系。

更新日期:2020-07-28
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