Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes. In this work, we leverage Multiple Instance Learning (MIL) to overcome this issue, and propose a new framework called MILLET: Multiple Instance Learning for Locally Explainable Time series classification. We apply MILLET to existing deep learning TSC models and show how they become inherently interpretable without compromising (and in some cases, even improving) predictive performance. We evaluate MILLET on 85 UCR TSC datasets and also present a novel synthetic dataset that is specially designed to facilitate interpretability evaluation. On these datasets, we show MILLET produces sparse explanations quickly that are of higher quality than other well-known interpretability methods. To the best of our knowledge, our work with MILLET, which is available on GitHub (https://github.com/JAEarly/MILTimeSeriesClassification), is the first to develop general MIL methods for TSC and apply them to an extensive variety of domains

翻译：传统的时间序列分类方法通常是黑箱模型，难以揭示其决策过程的内在解释。为解决这一问题，本文利用多实例学习（Multiple Instance Learning, MIL）提出新框架MILLET：基于多实例学习的局部可解释时间序列分类。我们将MILLET应用于现有深度学习时间序列分类模型，并证明其在不牺牲预测性能（某些情况下甚至有所提升）的前提下，实现了内在可解释性。我们在85个UCR时间序列分类数据集上评估MILLET，并构建了一个专门用于评估可解释性的新型合成数据集。在这些数据集上，结果显示MILLET能够快速生成稀疏解释，且解释质量优于其他知名可解释性方法。据我们所知，本文提出的MILLET（代码已开源：https://github.com/JAEarly/MILTimeSeriesClassification）是首个为时间序列分类开发通用多实例学习方法，并将其广泛应用于多个领域的相关工作。