Feature extraction by grammatical evolution for one‑class time series classifcation

When dealing with a new time series classifcation problem, modellers do not know in advance which features could enable the best classifcation performance. We propose an evolutionary algorithm based on grammatical evolution to attain a data driven feature-based representation of time series with minimal human intervention. The proposed algorithm can select both the features to extract and the sub-sequences from which to extract them. These choices not only impact classifcation perfor mance but also allow understanding of the problem at hand. The algorithm is tested on 30 problems outperforming several benchmarks. Finally, in a case study related to subject authentication, we show how features learned for a given subject are able to generalise to subjects unseen during the extraction phase.