Learning higher accuracy decision trees from concept drifting data streams

Satoru Nishimura; Masahiro Terabe; Kazuo Hashimoto; Koichiro Mihara

doi:10.1007/978-3-540-69052-8_19

Learning higher accuracy decision trees from concept drifting data streams

Satoru Nishimura^*, Masahiro Terabe, Kazuo Hashimoto, Koichiro Mihara

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 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 CVFDT_NBC, 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 CVFDT_NBC can induce a decision tree with more accuracy than CVFDT does.

Original language	English
Title of host publication	New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings
Pages	179-188
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-540-69052-8_19
Publication status	Published - 2008
Externally published	Yes
Event	21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008 - Wroclaw, Poland Duration: 2008 Jun 18 → 2008 Jun 20

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	5027 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008
Country/Territory	Poland
City	Wroclaw
Period	08/6/18 → 08/6/20

Keywords

CVFDT
Concept drift
Data stream
Decision tree
Naive-Bayes classifiers

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-69052-8_19

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

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

New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings. 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 proceeding › Conference contribution

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. 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, Poland, 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 New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings. 2008. p. 179-188. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-69052-8_19

Nishimura, Satoru ; Terabe, Masahiro ; Hashimoto, Kazuo et al. / Learning higher accuracy decision trees from concept drifting data streams. New Frontiers in Applied Artificial Intelligence - 21st International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2008, Proceedings. 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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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.",

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AB - 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.

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Learning higher accuracy decision trees from concept drifting data streams

Abstract

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Keywords

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