Interval and Dynamic Time Warping-based Decision Trees

Abstract

This work presents decision trees adequate for the classification of series data. There are several methods for this task, but most of them focus on accuracy. One of the requirements of data mining is to produce comprehensible models. Decision trees are one of the most comprehensible classifiers. The use of these methods directly on this kind of data is, generally, not adequate, because complex and inaccurate classifiers are obtained. Hence, instead of using the raw features, new ones are constructed