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 (this https URL), is the
first to develop general MIL methods for TSC and apply them to an extensive
variety of domains

我们利用多示例学习 (MIL) 提出了一种名为 MILLET 的新框架，将其应用于现有的深度学习时间序列分类模型，展示了如何在不降低 (有时甚至提高) 预测性能的情况下使其具有内在的可解释性。通过在 85 个 UCR 时间序列分类数据集上进行评估，并提出了一个新的合成数据集，我们展示了 MILLET 能够快速生成比其他著名的解释性方法更高质量且更稀疏的解释。据我们所知，我们的工作是首次开发用于时间序列分类的通用 MIL 方法，并将其应用于广泛领域。

通过多实例学习实现固有可解释的时间序列分类

Inherently Interpretable Time Series Classification via Multiple  Instance Learning

Clinical notes are text documents that are created by clinicians for each
patient encounter. They are typically accompanied by medical codes, which
describe the diagnosis and treatment. Annotating these codes is labor intensive
and error prone; furthermore, the connection between the codes and the text is
not annotated, obscuring the reasons and details behind specific diagnoses and
treatments. We present an attentional convolutional network that predicts
medical codes from clinical text. Our method aggregates information across the
document using a convolutional neural network, and uses an attention mechanism
to select the most relevant segments for each of the thousands of possible
codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of
0.54, which are both better than the prior state of the art. Furthermore,
through an interpretability evaluation by a physician, we show that the
attention mechanism identifies meaningful explanations for each code assignment

本文提出了一种使用卷积神经网络和注意力机制对临床文本进行医学编码的方法，并展示了该方法在精度和可解释性方面的优势。