Support vector machines of interval-based features for time series classification

The obtained classifiers were simply a linear combination of literals, so it is natural to expect some improvements in the results if those literals were combined in more complex ways. In this work we explore the possibility of using the literals selected by the boosting algorithm as new features, and then using a SVM with these metafeatures. The experimental results show the validity of the proposed method.