Abstract In several real-world classification problems it can be impractical to collect samples from classes other than the one of interest, hence the need for classifiers trained on a single class. There is a rich literature concerning binary and multi-class time series classification but less concerning one-class learning. In this study, we investigate the little-explored one-class time series classification problem. We represent time series as vectors of dissimilarities from a set of time series referred to as prototypes. Based on this approach, we evaluate a Cartesian product of 12 dissimilarity measures, and 8 prototype methods (strategies to select prototypes). Finally, a one-class nearest neighbor classifier is used on the dissimilarity-based representations (DBR). Experimental results show that DBR are competitive overall when compared with a strong baseline on the data-sets of the UCR/UEA archive. Additionally, DBR enable dimensionality reduction, and visual exploration of data-sets.

中文翻译：

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时间序列上一类分类的基于相异性的表示

摘要 在几个现实世界的分类问题中，从感兴趣的类别以外的类别中收集样本是不切实际的，因此需要对单个类别进行训练的分类器。关于二元和多类时间序列分类的文献很多，但关于一类学习的文献较少。在这项研究中，我们调查了鲜为人知的一类时间序列分类问题。我们将时间序列表示为一组被称为原型的时间序列的不同点的向量。基于这种方法，我们评估了 12 种不同度量和 8 种原型方法（选择原型的策略）的笛卡尔积。最后，一类最近邻分类器用于基于相异性的表示（DBR）。实验结果表明，与 UCR/UEA 档案数据集的强基线相比，DBR 总体上具有竞争力。此外，DBR 支持降维和数据集的可视化探索。