Fuzzy Decision Tree

نویسندگان

  • Jooyeol Yun
  • Jun won Seo
  • Taeseon Yoon
چکیده

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.

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تاریخ انتشار 2014