Decision-Tree Induction from Time-Series Data Based on a Standard-Example Split Test
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. ICML Proceedings of the Twentieth International Conference on Machine Learning
Cite
Text
Yamada et al. "Decision-Tree Induction from Time-Series Data Based on a Standard-Example Split Test." International Conference on Machine Learning, 2003.Markdown
[Yamada et al. "Decision-Tree Induction from Time-Series Data Based on a Standard-Example Split Test." International Conference on Machine Learning, 2003.](https://mlanthology.org/icml/2003/yamada2003icml-decision/)BibTeX
@inproceedings{yamada2003icml-decision,
title = {{Decision-Tree Induction from Time-Series Data Based on a Standard-Example Split Test}},
author = {Yamada, Yuu and Suzuki, Einoshin and Yokoi, Hideto and Takabayashi, Katsuhiko},
booktitle = {International Conference on Machine Learning},
year = {2003},
pages = {840-847},
url = {https://mlanthology.org/icml/2003/yamada2003icml-decision/}
}