Research Area:  Machine Learning
When dealing with a new time series classification problem, modellers do not know in advance which features could enable the best classification 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 classification performance 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.
Time Series Classification
Author(s) Name:  Stefano Mauceri, James Sweeney, Miguel Nicolau & James McDermott
Journal name:  Genetic Programming and Evolvable Machines
Publisher name:  Springer
Volume Information:  volume 22, pages 267–295 (2021)
Paper Link:   https://link.springer.com/article/10.1007/s10710-021-09403-x