A novel distance and similarity measures on hesitant fuzzy sets with applications in pattern recognition
عنوان مقاله: A novel distance and similarity measures on hesitant fuzzy sets with applications in pattern recognition
شناسه ملی مقاله: CSCG05_104
منتشر شده در پنجمین کنفرانس بین المللی محاسبات نرم در سال 1402
شناسه ملی مقاله: CSCG05_104
منتشر شده در پنجمین کنفرانس بین المللی محاسبات نرم در سال 1402
مشخصات نویسندگان مقاله:
M Najafi - Department of Mathematics, Velayat University;
A. Khosravi Tanak - Department of Statistics, Velayat University
خلاصه مقاله:
M Najafi - Department of Mathematics, Velayat University;
A. Khosravi Tanak - Department of Statistics, Velayat University
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.
کلمات کلیدی: Hesitant fuzzy set،distance measure،similarity measure،hesitancy index،pattern recognition
صفحه اختصاصی مقاله و دریافت فایل کامل: https://civilica.com/doc/1966960/