The Continuous-Function Attribute Class in Decision Tree Induction

Authors:
Michael Boronowsky
Affiliations:
-
Venue:
DS '98 Proceedings of the First International Conference on Discovery Science
Year:
1998

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Abstract

The automatic extraction of knowledge from data gathered from a dynamic system is an important task, because continuous measurement acquisition provides an increasing amount of numerical data. On an abstract layer these data can generally be modeled as continuous functions over time. In this article we present an approach to handle continuous-function attributes efficiently in decision tree induction, if the entropy minimalization heuristics is applied. It is shown how time series based upon continuous functions could be preprocessed if used in decision tree induction. A proof is given, that a piecewise linear approximation of the individual time series or the underlying continuous functions could improve the efficiency of the induction task.