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

Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi

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

41 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings, Twentieth International Conference on Machine Learning
EditorsT. Fawcett, N. Mishra
Pages840-847
Number of pages8
Publication statusPublished - Dec 1 2003
Externally publishedYes
EventProceedings, Twentieth International Conference on Machine Learning - Washington, DC, United States
Duration: Aug 21 2003Aug 24 2003

Publication series

NameProceedings, Twentieth International Conference on Machine Learning
Volume2

Other

OtherProceedings, Twentieth International Conference on Machine Learning
Country/TerritoryUnited States
CityWashington, DC
Period8/21/038/24/03

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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