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

Yuu Yamada, Einoshin Suzuki, Hideto Yokoi, and Katsuhiko Takabayashi

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.


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