Experimental evaluation of time-series decision tree

Authors:
Yuu Yamada;Einoshin Suzuki;Hideto Yokoi;Katsuhiko Takabayashi
Affiliations:
Electrical and Computer Engineering, Yokohama National University, Yokohama, Japan;Electrical and Computer Engineering, Yokohama National University, Yokohama, Japan;Division for Medical Informatics, Chiba-University Hospital, Chiba, Japan;Division for Medical Informatics, Chiba-University Hospital, Chiba, Japan
Venue:
AM'03 Proceedings of the Second international conference on Active Mining
Year:
2003

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Abstract

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.