Support vector machines of interval-based features for time series classification
In previous works, a time series classification system has been presented. It is based on boosting very simple classifiers, formed only by one literal. The used literals are based on temporal intervals. 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. (c) 2005 Elsevier B.V. All rights reserved.
time series classification boosting kernel methods