With the advent of fast data streams, real-time machine learning has become a challenging task, demanding many processing resources. In addition, they can be affected by the concept drift effect, by which learning methods have to detect changes in the data distribution and adapt to these evolving conditions. Several emerging paradigms such as the so-called Smart Dust, Utility Fog, or Swarm Robotics are in need for efficient and scalable solutions in real-time scenarios, and where usually computing resources are constrained. Cellular automata, as low-bias and robust-to-noise pattern recognition methods with competitive classification performance, meet the requirements imposed by the aforementioned paradigms mainly due to their simplicity and parallel nature. In this work we propose LUNAR, a streamified version of cellular automata devised to successfully meet the aforementioned requirements. LUNAR is able to act as a real incremental learner while adapting to drifting conditions. Furthermore, LUNAR is highly interpretable, as its cellular structure represents directly the mapping between the feature space and the labels to be predicted. 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.

随着快速数据流的出现，实时机器学习已成为一项具有挑战性的任务，需要大量处理资源。此外，它们可能会受到概念漂移效应的影响，通过这种漂移，学习方法必须检测数据分布的变化并适应这些不断发展的条件。在实时场景中通常需要限制计算资源的情况下，需要一些新兴的范例，例如所谓的“智能尘埃”，“实用雾”或“群机器人”，以提供高效且可扩展的解决方案。细胞自动机作为具有竞争性分类性能的低偏差和鲁棒噪声模式识别方法，主要由于其简单性和并行性而满足了上述范例的要求。在这项工作中，我们提出了LUNAR，流式细胞自动机的设计目的是成功满足上述要求。LUNAR能够充当真正的增量学习者，同时适应漂移条件。此外，LUNAR是高度可解释的，因为其细胞结构直接表示特征空间和要预测的标签之间的映射。与已建立且成功的在线学习方法相比，使用合成数据和真实数据进行的大量模拟将提供其分类性能方面的竞争行为的证据。