  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.
