Ranking Faculties in Universities by Using Fuzzy Hierarchical Approach

Ranking teachers’ performance is a very important problem in universities using imperfect information. There are set of methods for the evaluation performance of teachers: Fuzzy logic, Neural networks, Fuzzy Neural Network, Decision Trees, Multilayer perceptron, Naïve Bayes Classifier, Fuzzy clustering, Random Forest Classifier, Logistic Regression, Support Vector Machine, Associative classification model, K-Nearest-Neighbor, etc. The methods in the scientific literature on teacher evaluation partially use imperfect information, but the information used for teacher evaluation is imperfect. In this case, it has been proven in the scientific literature that the best descriptive tool is Z number. This paper uses a hierarchical approach and solves the teacher evaluation problem which is based on the Z-valued decision-making problem.