Learning higher accuracy decision trees from concept drifting data streams

Satoru Nishimura, Masahiro Terabe, Kazuo Hashimoto, Koichiro Mihara

研究成果: Conference contribution

9 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトルNew Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings
ページ179-188
ページ数10
DOI
出版ステータスPublished - 2008 8 4
外部発表はい
イベント21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008 - Wroclaw, Poland
継続期間: 2008 6 182008 6 20

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
5027 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)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

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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