A Classifier Based on a Decision Tree with Temporal Cuts

A new method of decision tree construction from temporal data is proposed in the paper. This method uses the so-called temporal cuts for binary partition of data in tree nodes. The novelty of the proposed approach is that the quality of cuts is calculated not on the basis of the discernibility of objects (related to time points), but on the basis of the discernibility of time windows labeled with different decision classes. The paper includes results of experiments performed on our data sets and collections from machine learning repositories. In order to evaluate the presented method, we compared its performance with the classification results of a local discretization decision tree, and other methods well known from literature. Our new method outperforms these known methods.