Genetic Programming and Evolvable Machines ( IF 2.6 ) Pub Date : 2021-04-23 , DOI: 10.1007/s10710-021-09403-x Stefano Mauceri , James Sweeney , Miguel Nicolau , James McDermott
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.
中文翻译:
一类时间序列分类的语法进化特征提取
在处理新的时间序列分类问题时,建模者事先不知道哪些功能可以实现最佳分类性能。我们提出一种基于语法进化的进化算法,以最少的人工干预即可获得基于数据驱动的基于时间序列的特征表示。所提出的算法既可以选择要提取的特征,又可以从中提取子序列。这些选择不仅会影响分类性能,还可以帮助您了解当前的问题。该算法在优于几个基准的30个问题上进行了测试。最后,在与主题身份验证相关的案例研究中,我们展示了如何为给定主题学习的功能可以推广到提取阶段未看到的主题。