An improved cluster oriented fuzzy decision trees

Shan SU, Xizhao WANG, Junhai ZHAI

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationRough Sets, Fuzzy Sets, Data Mining and Granular Computing : 12th International Conference, RSFDGrC 2009, Proceedings
EditorsHiroshi SAKAI, Mihir Kumar CHAKRABORTY, Aboul Ella HASSANIEN, Dominik ŚLĘZAK, William ZHU
PublisherSpringer-Verlag, Berlin, Heidelberg
Pages447-454
Number of pages8
ISBN (Electronic)9783642106460
ISBN (Print)9783642106453
DOIs
Publication statusPublished - 2009
Externally publishedYes
Event12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009 - Delhi, India
Duration: 15 Dec 200918 Dec 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume5908
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2009
Country/TerritoryIndia
CityDelhi
Period15/12/0918/12/09

Keywords

  • Cluster oriented decision trees
  • Decision tree
  • FCM
  • Node splitting criteria

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