Distance and similarity measures for dual hesitant fuzzy sets and their applications in pattern recognition

被引：67
|
作者：
Su, Zhan [1 ]
Xu, Zeshui [2 ]
Liu, Haifeng [1 ]
Liu, Shousheng [1 ]
机构：
[1] PLA Univ Sci & Technol, Coll Sci, Nanjing, Jiangsu, Peoples R China
[2] Sichuan Univ, Sch Business, Chengdu 610064, Peoples R China
来源：
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS | 2015年 / 29卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Dual hesitant fuzzy set (DHFS); hesitant fuzzy set (HFS); distance measures; similarity measures; pattern recognition; AGGREGATION;
D O I：
10.3233/IFS-141474
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Dual hesitant fuzzy set (DHFS) is a very comprehensive set which includes fuzzy set, intuition fuzzy set and hesitant fuzzy set as its special cases. Distance and similarity measures play great roles in many areas, such as decision making, pattern recognition, etc. In this paper, we introduce some distance and similarity measures for DHFSs based on Hamming distance, Euclidean distance and Hausdorff distance. Two examples are used to illustrate these distance and similarity measures and their applications in pattern recognition. Finally, the comparisons among DHFSs and the corresponding IVIFSs and HFSs are made in detail by utilizing the developed distance measures.
引用
收藏
页码：731 / 745
页数：15
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