An evolutionary and attribute-oriented ensemble classifier

Chien I. Lee, Cheng Jung Tsai, Chih Wei Ku

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In the research area of decision tree, numerous researchers have been focusing on improving the predictive accuracy. However, obvious improvement can hardly be made until the introduction of the ensemble classifier. In this paper, we propose an Evolutionary Attribute-Oriented Ensemble Classifier (EAOEC) to improve the accuracy of sub-classifiers and at the same time maintain the diversity among them. EAOEC uses the idea of evolution to choose proper attribute subset for the building of every sub-classifier. To avoid the huge computation cost for the evolution, EAOEC uses the gini value gained during the construction of a sub-tree as the evolution basis to build the next sub-tree. Eventually, EAOEC classifier uses uniform weight voting to combine all sub-classifiers and experiments show that EAOEC can efficiently improve the predictive accuracy.

Original languageEnglish
Title of host publicationComputational Science and Its Applications - ICCSA 2006
Subtitle of host publicationInternational Conference, Proceedings - Part II
PublisherSpringer Verlag
Number of pages9
ISBN (Print)3540340726, 9783540340720
Publication statusPublished - 2006 Jan 1
EventICCSA 2006: International Conference on Computational Science and Its Applications - Glasgow, United Kingdom
Duration: 2006 May 82006 May 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3981 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherICCSA 2006: International Conference on Computational Science and Its Applications
CountryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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