Fuzzy classification as a decision making problem in hesitant environments

This paper presents a new approach for fuzzy classification in the hesitant environments (FCHE) by decision making process. Our intention of the hesitant environments is situations, which there are different evaluations of experts for one problem. In this paper, we focus on cases that a classifier can be designed by knowledge of experts while each expert can classify data with a feature, independently, by linguistic terms. In this paper, we assume the classification task as a decision making problem in which, each feature as an attribute, each class as an alternative and each expert as a decision maker are considered. In the new classifier, we can use different score functions and aggregation operators in hesitant fuzzy sets for fuzzy classification in various viewpoints. Finally, our new approach is applied to a practical problem in economics, then for validation of the proposed model, we use iris data from the UCI repository.