Automatic Measurement Interpretation of a Physical System with Decision Tree Induction

Abstract

. The automatic interpretation of data is a problem with great practical importance. Due to the need of process control, a physical system is generally equipped with a great number of sensors. But how could the acquired data be used to improve the insights into the system dynamics ? In this article decision tree induction is used to extract knowledge out of the continuous measurements of a physical system. The introduced approach tries to minimize manual user interaction, because in practical applications people usually are not willing or able to invest too much time in measurement interpretation. As continuous monitoring of a physical system provides a great amount of data, we show how the efficiency in induction could be increased, by abstracting time series though piecewise linear approximations. In two case studies the practical importance of our approach is illustrated. On the one hand it is demonstrated, that universally valid rules can be extracted and on the other hand it is sh..