Although the one nearest neighbor approach is widely used in time series classification, its successful performance requires enough labeled data, which is often difficult to obtain due to a high labeling cost. This article considers a practical classification scenario in which labeled data are scant but unlabeled data are plenty, and a limited budget for the annotating task is provided. For an effective classification with limited resources, we propose a nearest neighbor-based sampling strategy for active learning. The proposed approach uses highly local information to measure the uncertainty and utility of an unlabeled instance and is applicable to extremely sparse labeled data. Furthermore, we extend the proposed approach to batch mode active learning to select a batch of informative samples at each sampling iteration. Experimental results on the WAFER and ECG5000 data sets demonstrate the effectiveness of the proposed algorithm as compared with other nearest neighbor-based approaches.

尽管在时间序列分类中广泛使用了一种最接近的方法，但其成功的性能需要足够的标记数据，由于标记成本高，通常难以获得该数据。本文考虑了一种实际的分类方案，其中标记的数据很少，但未标记的数据很多，并且为注释任务提供了有限的预算。为了在资源有限的情况下进行有效分类，我们提出了一种基于最近邻的主动学习抽样策略。所提出的方法使用高度本地化的信息来度量未标记实例的不确定性和实用性，并且适用于极为稀疏的标记数据。此外，我们将提出的方法扩展到批处理模式主动学习，以在每次采样迭代时选择一批信息量大的样本。