Experimental evaluation of time-series decision tree

Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, Katsuhiko Takabayashi

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

5 Citations (Scopus)


In this paper, we give experimental evaluation of our time-series decision tree induction method under various conditions. 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. It has been empirically observed that the method induces accurate and comprehensive decision trees in time-series classification, which has gaining increasing attention due to its importance in various real-world applications. The evaluation has revealed several important findings including interaction between a split test and its measure of goodness.

Original languageEnglish
Title of host publicationActive Mining - Second International Workshop, AM 2003, Revised Selected Papers
PublisherSpringer Verlag
Number of pages20
ISBN (Print)3540261575, 9783540261575
Publication statusPublished - 2005
Externally publishedYes
EventSecond International Workshop on Active Mining, AM 2003 - Maebashi, Japan
Duration: Oct 28 2003Oct 31 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3430 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherSecond International Workshop on Active Mining, AM 2003

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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