Shapelets are discriminative segments used to classify time-series instances. Shapelet methods that jointly learn both classifiers and shapelets have been studied in recent years because such methods provide both interpretable results and superior accuracy. The partial area under the receiver operating characteristic curve (pAUC) for a low range of false-positive rates (FPR) is an important performance measure for practical cases in industries such as medicine, manufacturing, and maintenance. In this article, we propose a method that jointly learns both shapelets and a classifier for pAUC optimization in any FPR range, including the full AUC. In addition, we propose the following two extensions for shapelet methods: (1) reducing algorithmic complexity in time-series length to linear time and (2) explicitly determining the classes that shapelets tend to match. Comparing with state-of-the-art learning-based shapelet methods, we demonstrated the superiority of pAUC on UCR time-series data sets and its effectiveness in industrial case studies from medicine, manufacturing, and maintenance.

中文翻译：

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LTSpAUC：学习用于部分 AUC 最大化的时间序列 Shapelets

Shapelets 是用于对时间序列实例进行分类的判别性片段。近年来已经研究了联合学习分类器和 shapelet 的 Shapelet 方法，因为这些方法提供了可解释的结果和卓越的准确性。低假阳性率 (FPR) 范围内的受试者工作特征曲线 (pAUC) 下的局部面积是医学、制造和维护等行业实际案例的重要性能指标。在本文中，我们提出了一种联合学习 shapelet 和分类器的方法，用于在任何 FPR 范围内进行 pAUC 优化，包括完整的 AUC。此外，我们提出了以下两个 shapelet 方法的扩展：(1) 将时间序列长度的算法复杂性降低到线性时间，以及 (2) 明确确定 shapelet 倾向于匹配的类。与最先进的基于学习的 shapelet 方法相比，我们证明了 pAUC 在 UCR 时间序列数据集上的优越性及其在医学、制造和维护的工业案例研究中的有效性。