Feature extraction by grammatical evolution for one-class time series classification

被引：0

作者：

Stefano Mauceri

James Sweeney

Miguel Nicolau

James McDermott

机构：

[1] University College Dublin,Natural Computing Research and Applications Group (NCRA)

[2] University of Limerick,undefined

[3] University College Dublin,undefined

[4] National University of Ireland,undefined

来源：

Genetic Programming and Evolvable Machines | 2021年 / 22卷

关键词：

Evolutionary computation; One-class classification; Time series;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：267 / 295

页数：28

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