Classification based on the trace of variables over time

Authors:
Frank Höppner;Alexander Topp
Affiliations:
University of Applied Sciences Braunschweig/Wolfenbüttel, Wolfsburg, Germany;University of Applied Sciences Braunschweig/Wolfenbüttel, Wolfsburg, Germany
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

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Abstract

To be successful with certain classification problems or knowledge discovery tasks it is not sufficient to look at the available variables at a single point in time, but their development has to be traced over a period of time. It is shown that patterns and sequences of labeled intervals represent a particularly well suited data format for this purpose. An extension of existing classifiers is proposed that enables them to handle this kind of sequential data. Compared to earlier approaches the expressiveness of the pattern language (using Allen et al.'s interval relationships) is increased, which allows the discovery of many temporal patterns common to real-world applications.