The New Approach on Fuzzy Decision Trees
This work addresses the challenge of fuzzy data in machine learning for applications like classification, but it is incremental as it builds on existing decision tree methods.
The paper tackled the problem of handling fuzzy and uncertain data in decision trees by proposing a fuzzy decision tree induction method, achieving an accuracy improvement compared to the ID3 algorithm on the iris flower dataset.
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.