  State-of-the-art machine learning solutions mainly focus on creating highly
accurate models without constraints on hardware resources. Stream mining
algorithms are designed to run on resource-constrained devices, thus a focus on
low power and energy and memory-efficient is essential. The Hoeffding tree
algorithm is able to create energy-efficient models, but at the cost of less
accurate trees in comparison to their ensembles counterpart. Ensembles of
Hoeffding trees, on the other hand, create a highly accurate forest of trees
but consume five times more energy on average. An extension that tried to
obtain similar results to ensembles of Hoeffding trees was the Extremely Fast
Decision Tree (EFDT). This paper presents the Green Accelerated Hoeffding Tree
(GAHT) algorithm, an extension of the EFDT algorithm with a lower energy and
memory footprint and the same (or higher for some datasets) accuracy levels.
GAHT grows the tree setting individual splitting criteria for each node, based
on the distribution of the number of instances over each particular leaf. The
results show that GAHT is able to achieve the same competitive accuracy results
compared to EFDT and ensembles of Hoeffding trees while reducing the energy
consumption up to 70%.
