Hoeffding Trees with nmin adaptation

TL;DR

This paper introduces an adaptive nmin parameter for Hoeffding trees, significantly reducing energy consumption in data stream mining with minimal impact on accuracy.

Contribution

The paper proposes the nmin adaptation method for Hoeffding trees, enabling dynamic parameter adjustment to improve energy efficiency.

Findings

VFDT-nmin reduces energy consumption by up to 27% compared to VFDT.

VFDT-nmin reduces energy consumption by up to 92% compared to CVFDT.

Accuracy is marginally affected in some datasets.

Abstract

Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient. In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin parameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.