Decision-tree Induction from Time-series Data Based on a Standard-example Split Test

Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

42 被引用数 (Scopus)

抄録

This paper proposes a novel decision tree for a data set with time-series attributes. Our time-series tree has a value (i.e. a time sequence) of a time-series attribute in its internal node, and splits examples based on dissimilarity between a pair of time sequences. Our method selects, for a split test, a time sequence which exists in data by exhaustive search based on class and shape information. Experimental results confirm that our induction method constructs comprehensive and accurate decision trees. Moreover, a medical application shows that our time-series tree is promising for knowledge discovery.

本文言語英語
ホスト出版物のタイトルProceedings, Twentieth International Conference on Machine Learning
編集者T. Fawcett, N. Mishra
ページ840-847
ページ数8
出版ステータス出版済み - 12 1 2003
外部発表はい
イベントProceedings, Twentieth International Conference on Machine Learning - Washington, DC, 米国
継続期間: 8 21 20038 24 2003

出版物シリーズ

名前Proceedings, Twentieth International Conference on Machine Learning
2

その他

その他Proceedings, Twentieth International Conference on Machine Learning
国/地域米国
CityWashington, DC
Period8/21/038/24/03

All Science Journal Classification (ASJC) codes

  • 工学(全般)

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引用スタイル