Abstract
Shapelet based classification is a promising time series classification approach, which usually results in accurate predictions that are competitive with sophisticated and more complex classifiers, while it provides interpretability for the predictions. An important step in classification using shapelets is to select candidate subsequences based on some evaluation criteria. We adapt the Silhouette score, used originally in the context of clustering, in order to rank and select shapelet candidates for classification. We demonstrate empirically that our approach is faster compared to other methods in the literature, while being competitive in terms of the accuracy of classification. In particular, when the number of shapelets used for classification is small, our approach is superior to all other evaluation methods.