An experimental evaluation for fuzzy decision trees

In this study, fuzzy decision tree which uses fuzzified data by using triangular or trapezoidal membership functions was developed and compared with classical decision tree. Basic and fuzzy version of splitting criteria were used. Moreover, effect of the linguistic terms that were used for fuzzified data was studied in this thesis. To achieve this aim, two different fuzzy datasets were obtained; winner linguistic term and all linguistic terms and compared with each other. The aim of this study is to improve classification performance of fuzzy decision trees and test the effect of the fuzzification of the dataset on the decision tree induction. The proposed fuzzy and classical decision tree methods based on ID3 decision tree algorithm was experimented on 18 datasets from UCI Machine Learning Repository. The experimental results of this study showed that, fuzzy decision tree is more successful than classical decision tree.