A bipolar interpretation of fuzzy decision trees

Tuan Fang Fan*, Churn Jung Liau, Duen-Ren Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Decision tree construction is a popular approach in data mining and machine learning, and some variants of decision tree algorithms have been proposed to deal with different types of data. In this paper, we present a bipolar interpretation of fuzzy decision trees. With the interpretation, various types of decision trees can be represented in a unified form. The edges of a fuzzy decision tree are labeled by fuzzy decision logic formulas and the nodes are split according to the satisfaction of these formulas in the data records. We present a construction algorithm for general fuzzy decision trees and show its application to different types of training data.

Original languageEnglish
Title of host publicationData Mining
Subtitle of host publicationFoundations and Practice
Number of pages15
StatePublished - 15 Sep 2008

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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