Learning higher accuracy decision trees from concept drifting data streams

Satoru Nishimura, Masahiro Terabe, Kazuo Hashimoto, Koichiro Mihara

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

8 Citations (Scopus)

Abstract

In this paper, we propose to combine the naive-Bayes approach with CVFDT, which is known as one of the major algorithms to induce a high-accuracy decision tree from time-changing data streams. The proposed improvement, called CVFDTNBC, induces a decision tree as CVFDT does, but contains naive-Bayes classifiers in the leaf nodes of the induced decision tree. The experiment using the artificially generated time-changing data streams shows that CVFDTNBC can induce a decision tree with more accuracy than CVFDT does.

Original languageEnglish
Title of host publicationNew Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings
Pages179-188
Number of pages10
DOIs
Publication statusPublished - 2008 Aug 4
Externally publishedYes
Event21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008 - Wroclaw, Poland
Duration: 2008 Jun 182008 Jun 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5027 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008
CountryPoland
CityWroclaw
Period08/6/1808/6/20

Keywords

  • CVFDT
  • Concept drift
  • Data stream
  • Decision tree
  • Naive-Bayes classifiers

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Nishimura, S., Terabe, M., Hashimoto, K., & Mihara, K. (2008). Learning higher accuracy decision trees from concept drifting data streams. In New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings (pp. 179-188). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5027 LNAI). https://doi.org/10.1007/978-3-540-69052-8_19