The New Approach on Fuzzy Decision Trees
  Decision trees have been widely used in machine learning. However, due to
some reasons, data collecting in real world contains a fuzzy and uncertain
form. The decision tree should be able to handle such fuzzy data. This paper
presents a method to construct fuzzy decision tree. It proposes a fuzzy
decision tree induction method in iris flower data set, obtaining the entropy
from the distance between an average value and a particular value. It also
presents an experiment result that shows the accuracy compared to former ID3.
