Learning Higher Accuracy Decision Trees from Concept Drifting Data Streams

Authors:
Satoru Nishimura;Masahiro Terabe;Kazuo Hashimoto;Koichiro Mihara
Affiliations:
Graduate School of Information Science, Tohoku University, Sendai, Japan 980-8579;Graduate School of Information Science, Tohoku University, Sendai, Japan 980-8579;Graduate School of Information Science, Tohoku University, Sendai, Japan 980-8579;Graduate School of Information Science, Tohoku University, Sendai, Japan 980-8579
Venue:
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
Year:
2008

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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 CVFDTNBCcan induce a decision tree with more accuracy than CVFDT does.