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

Mondrian Forest for Data Stream Classification Under Memory Constraints