Abstract

Energy consumption reduction has been an increasing trend in machine learning over the past few years due to its socio-ecological importance. In new challenging areas such as edge computing, energy consumption and predictive accuracy are key variables during algorithm design and implementation. State-of-the-art ensemble stream mining algorithms are able to create highly accurate predictions at a substantial energy cost. This paper introduces the nmin adaptation method to ensembles of Hoeffding tree algorithms, to further reduce their energy consumption without sacrificing accuracy. We also present extensive theoretical energy models of such algorithms, detailing their energy patterns and how nmin adaptation affects their energy consumption. We have evaluated the energy efficiency and accuracy of the nmin adaptation method on five different ensembles of Hoeffding trees under 11 publicly available datasets. The results show that we are able to reduce the energy consumption significantly, by 21% on average, affecting accuracy by less than one percent on average.

中文翻译：

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Hoeffding树木乐团的能量建模

摘要

由于其社会生态重要性，在过去的几年中，降低能耗已成为机器学习的一种日益增长的趋势。在边缘计算等新的挑战性领域中，能耗和预测精度是算法设计和实施过程中的关键变量。最先进的集成流挖掘算法能够以高昂的能源成本创建高度准确的预测。本文将nmin自适应方法引入到Hoeffding树算法的集合中，以在不牺牲精度的情况下进一步降低其能量消耗。我们还介绍了此类算法的广泛理论能量模型，详细介绍了它们的能量模式以及nmin的适应方式影响他们的能源消耗。我们已经评估了11种可公开获得的数据集下5种不同的Hoeffding树组合的nmin自适应方法的能效和准确性。结果表明，我们能够显着降低能耗，平均降低21％，对精度的影响平均降低不到1％。