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 publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages179-188
Number of pages10
Volume5027 LNAI
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008 - Wroclaw
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)03029743
ISSN (Electronic)16113349

Other

Other21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008
CityWroclaw
Period08/6/1808/6/20

Fingerprint

Decision Trees
Decision trees
Data Streams
Decision tree
High Accuracy
Learning
Naive Bayes Classifier
Naive Bayes
Leaves
Classifiers
Concepts
Vertex of a graph
Experiment
Experiments

Keywords

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

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

Nishimura, S., Terabe, M., Hashimoto, K., & Mihara, K. (2008). Learning higher accuracy decision trees from concept drifting data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5027 LNAI, 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

Learning higher accuracy decision trees from concept drifting data streams. / Nishimura, Satoru; Terabe, Masahiro; Hashimoto, Kazuo; Mihara, Koichiro.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5027 LNAI 2008. p. 179-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5027 LNAI).

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

Nishimura, S, Terabe, M, Hashimoto, K & Mihara, K 2008, Learning higher accuracy decision trees from concept drifting data streams. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 5027 LNAI, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5027 LNAI, pp. 179-188, 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Wroclaw, 08/6/18. https://doi.org/10.1007/978-3-540-69052-8_19
Nishimura S, Terabe M, Hashimoto K, Mihara K. Learning higher accuracy decision trees from concept drifting data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5027 LNAI. 2008. p. 179-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-69052-8_19
Nishimura, Satoru ; Terabe, Masahiro ; Hashimoto, Kazuo ; Mihara, Koichiro. / Learning higher accuracy decision trees from concept drifting data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 5027 LNAI 2008. pp. 179-188 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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