A novel distance and similarity measures on hesitant fuzzy sets with applications in pattern recognition
محل انتشار: پنجمین کنفرانس بین المللی محاسبات نرم
سال انتشار: 1402
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 23
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شناسه ملی سند علمی:
CSCG05_104
تاریخ نمایه سازی: 9 اردیبهشت 1403
چکیده مقاله:
Distance and similarity measures are considered useful tools in a variety of scientific fields such as decision-making, pattern recognition, clustering analysis, medical diagnosis, etc. In this paper, we review the existing distance and similarity measures between hesitant fuzzy sets (HFSs) and show that in some cases they are not logical or efficient. So, we propose some improved distance and similarity measures for HFSs, considering the deviation degree as a hesitancy index for these sets. Comparing our novel measures with some existing distance measures shows that our proposed measures are reasonable and valid.
کلیدواژه ها:
نویسندگان
M Najafi
Department of Mathematics, Velayat University;
A. Khosravi Tanak
Department of Statistics, Velayat University