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

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Show simple item record Mauceri, Stefano Sweeney, James Nicolau, Miguel McDermott, James 2021-07-06T15:10:59Z 2021-07-06T15:10:59Z 2021
dc.description peer-reviewed en_US
dc.description.abstract 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. en_US
dc.language.iso eng en_US
dc.publisher Springer en_US
dc.relation.ispartofseries Genetic Programming and Evolvable Machines;
dc.subject evolutionary computation en_US
dc.subject one-class classification en_US
dc.subject time series en_US
dc.title Feature extraction by grammatical evolution for one‑class time series classifcation en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1007/s10710-021-09403-x
dc.contributor.sponsor ICON plc en_US
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US

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