On when is Reservoir Computing with Cellular Automata Beneficial?
This study addresses the applicability of ReCA for machine learning researchers, revealing limitations in task suitability.
The paper investigates the effectiveness of Reservoir Computing with Cellular Automata (ReCA) by showing it works in a simple implementation but fails on a time series classification task due to the encoding scheme alone, highlighting the need for ablation testing.
Reservoir Computing with Cellular Automata (ReCA) is a relatively novel and promising approach. It consists of 3 steps: an encoding scheme to inject the problem into the CA, the CA iterations step itself and a simple classifying step, typically a linear classifier. This paper demonstrates that the ReCA concept is effective even in arguably the simplest implementation of a ReCA system. However, we also report a failed attempt on the UCR Time Series Classification Archive where ReCA seems to work, but only because of the encoding scheme itself, not in any part due to the CA. This highlights the need for ablation testing, i.e., comparing internally with sub-parts of one model, but also raises an open question on what kind of tasks ReCA is best suited for.