LUNAR: Cellular Automata for Drifting Data Streams
This addresses the problem of efficient and scalable real-time learning for constrained environments like smart dust or swarm robotics, though it appears incremental as it adapts existing cellular automata to streaming contexts.
The authors tackled the challenge of real-time machine learning on fast, drifting data streams with limited computing resources by proposing LUNAR, a streamified cellular automata method, which demonstrated competitive classification performance in simulations with synthetic and real data.
With the advent of huges volumes of data produced in the form of fast streams, real-time machine learning has become a challenge of relevance emerging in a plethora of real-world applications. Processing such fast streams often demands high memory and processing resources. In addition, they can be affected by non-stationary phenomena (concept drift), by which learning methods have to detect changes in the distribution of streaming data, and adapt to these evolving conditions. A lack of efficient and scalable solutions is particularly noted in real-time scenarios where computing resources are severely constrained, as it occurs in networks of small, numerous, interconnected processing units (such as the so-called Smart Dust, Utility Fog, or Swarm Robotics paradigms). In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. It is able to act as a real incremental learner while adapting to drifting conditions. Extensive simulations with synthetic and real data will provide evidence of its competitive behavior in terms of classification performance when compared to long-established and successful online learning methods.