Supervised learning algorithms generally assume the availability of enough memory to store their data model during the training and test phases. However, in the Internet of Things, this assumption is unrealistic when data comes in the form of infinite data streams, or when learning algorithms are deployed on devices with reduced amounts of memory. In this paper, we adapt the online Mondrian forest classification algorithm to work with memory constraints on data streams. In particular, we design five out-of-memory strategies to update Mondrian trees with new data points when the memory limit is reached. Moreover, we design trimming mechanisms to make Mondrian trees more robust to concept drifts under memory constraints. We evaluate our algorithms on a variety of real and simulated datasets, and we conclude with recommendations on their use in different situations: the Extend Node strategy appears as the best out-of-memory strategy in all configurations, whereas different trimming mechanisms should be adopted depending on whether a concept drift is expected. All our methods are implemented in the OrpailleCC open-source library and are ready to be used on embedded systems and connected objects.

本文介绍了一种将监督学习算法从基于内存的模型转变为基于数据流处理，特别是针对存储容量有限的设备，使用了五种弥补内存限制的策略，并设计了修剪机制来应对数据概念漂移的在线Mondrian Forest (OMF)分类算法。作者在各种真实和模拟数据集上进行了实验，推荐使用Extend Node策略，修剪机制则需要根据是否预期概念漂移而定。所有方法都在OrpailleCC开源库上实现，并可用于嵌入式系统和物联网设备。

内存约束下的数据流分类Mondrian Forest