An Improved Cluster Oriented Fuzzy Decision Trees

Authors:
Shan Su;Xizhao Wang;Junhai Zhai
Affiliations:
Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, China 071002;Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, China 071002;Key Lab. of Machine Learning and Computational Intelligence, College of Mathematics and Computer Science, Hebei University, Baoding, China 071002
Venue:
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

Citing 6
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Abstract

In this paper, an improved cluster oriented decision trees algorithm shortly named ICFDT is presented. In this algorithm, fuzzy C-means clustering algorithm (FCM) without instance labels is used to split the nodes and two novel node expanding criteria are proposed. One criterion uses the ratio of homogenous samples in the node to split; the other splits the node by membership degree without labels. The experimental results in artificial and machine learning datasets show that our method can achieve better performance comparing to standard decision tree named C4.5.