Nowadays, the deployment of deep learning models on edge devices for addressing real-world classification problems is becoming more prevalent. Moreover, there is a growing popularity in the approach of early classification, a technique that involves classifying the input data after observing only an early portion of it, aiming to achieve reduced communication and computation requirements, which are crucial parameters in edge intelligence environments. While early classification in the field of time series analysis has been broadly researched, existing solutions for multivariate time series problems primarily focus on early classification along the temporal dimension, treating the multiple input channels in a collective manner. In this study, we propose a more flexible early classification pipeline that offers a more granular consideration of input channels and extends the early classification paradigm to the channel dimension. To implement this method, we utilize reinforcement learning techniques and introduce constraints to ensure the feasibility and practicality of our objective. To validate its effectiveness, we conduct experiments using synthetic data and we also evaluate its performance on real datasets. The comprehensive results from our experiments demonstrate that, for multiple datasets, our method can enhance the early classification paradigm by achieving improved accuracy for equal input utilization.

翻译：如今，在边缘设备上部署深度学习模型以解决现实世界分类问题正日益普遍。同时，早期分类（一种仅观察输入数据早期部分即进行分类的技术）方法因其能够降低通信和计算需求（这些是边缘智能环境中的关键参数）而愈发流行。尽管时间序列分析领域的早期分类已得到广泛研究，但现有的多变量时间序列问题解决方案主要集中于时间维度的早期分类，将多个输入通道以集体方式处理。在本研究中，我们提出了一种更灵活的早期分类流程，该流程能够更细致地考虑输入通道，并将早期分类范式扩展至通道维度。为实现该方法，我们利用强化学习技术并引入约束条件，以确保目标的可行性和实用性。为验证其有效性，我们使用合成数据进行了实验，同时在真实数据集上评估了其性能。实验的综合结果表明，对于多个数据集，我们的方法能够通过实现同等输入利用下的更高准确率，从而增强早期分类范式。