期刊名称：International Journal of Networking and Computing
印刷版ISSN：2185-2847
出版年度：2021
卷号：11
期号：2
页码：215-230
语种：English
出版社：International Journal of Networking and Computing
摘要：Training machine learning models on edge devices is always a conflict with power consumption and computing cost. This paper introduces a hardware-oriented training method called ExtraFerns for a unique subset of decision tree ensembles, which significantly decreases memory access and optimizes each tree in parallel. ExtraFerns benefits from the advantages of both extraTrees and randomFerns. As extraTrees does, it generates nodes by randomly selecting attributes and generating thresholds. Then, as randomFerns does, it builds ferns, which are decision trees that share identical nodes at each depth. In contrast to other ensemble methods using greedy optimization, ExtraFerns attempts global optimization of each fern. Experimental results show that ExtraFerns requires only 4.3% and 4.1% memory access for training models with 3.0% and 1.2% accuracy drops compared with randomForest and extraTrees, respectively. This paper also proposes applying lightweight random projection to ExtraFerns as a preprocessing step, which achieved a further accuracy improvement of up to 2.0% for image datasets.
关键词：ensemble learning;fern ensemble;decision tree ensemble;non-greedy optimization;parallel optimization;random projection
