A bipolar interpretation of fuzzy decision trees

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

*此作品的通信作者

研究成果: Chapter同行評審

摘要

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.

原文English
主出版物標題Data Mining
主出版物子標題Foundations and Practice
頁面109-123
頁數15
DOIs
出版狀態Published - 15 9月 2008

出版系列

名字Studies in Computational Intelligence
118
ISSN(列印)1860-949X

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