Zahra Behdani - Department of Mathematics and Statistics, Faculty of Data Sciences and Energy, Behbahan Khatam Alanbia university of Technology, Khouzestan, Iran
Majid Darehmiraki - Department of Mathematics and Statistics, Faculty of Data Sciences and Energy, Behbahan Khatam Alanbia university of Technology, Khouzestan, Iran

Pattern recognition and classification are two areas in which the K-Nearest Neighbormethod is considered to be one of the most straightforward intelligent algorithms.As the complexity of practical applications continues to grow, there is an increasein the amount of ambiguity and fuzziness. The purpose of this research is to buildthe evidence k-Nearest Neighbor under the hesitant fuzzy environment. To do so, wemake use of the hesitant fuzzy set (HFS) in order to express unclear preferences andinformation. Additionally, a numerical example associated with a classification issueis offered in order to assess the effectiveness of the strategy that has been suggested.

Hesitant fuzzy, K Nearest Neighbor, Distance measure