Green Accelerated Hoeffding Tree

TL;DR

The paper introduces GAHT, an energy-efficient extension of EFDT, which maintains high accuracy while reducing energy consumption by up to 70% in stream mining tasks.

Contribution

GAHT extends EFDT with node-specific splitting criteria, achieving similar accuracy to ensembles but with significantly lower energy and memory use.

Findings

GAHT reduces energy consumption by up to 70%.

GAHT maintains comparable or higher accuracy than EFDT.

GAHT has a lower memory footprint than ensemble methods.

Abstract

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%.