Hoeffding Trees with Nmin Adaptation

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.Index Terms-data stream mining, green artificial intelligence, energy efficiency, hoeffding trees, energy aware machine learning

Energy modeling of Hoeffding tree ensembles

“…embedded devices) where energy and battery consumption is the main concern. This work is an extension of an already published study where we introduced nmin adaptation [18]. While in that work the nmin adaptation method was only applied to the standard version of the Hoeffding tree algorithm, this study proposes more energy efficient approaches to create ensembles of Hoeffding trees, validated by the experiments on five different algorithms and 11 different datasets.…”

“…The nmin adaptation method was previously introduced in [18]. While that study only focused on the energy reduction of standard Hoeffding tree algorithms, this study proposes to use the nmin adaptation method on ensembles of Hoeffding trees, to align with the current approaches in data stream mining.…”

“…The nmin adaptation method is a method presented in [18], which reduces the energy consumption of standard Hoeffding tree algorithms by adapting the number of instances needed to create a split. We extend that study by incorporating the nmin adaptation method to ensembles of Hoeffding trees.…”

Enhancement of Very Fast Decision Tree for Data Stream Mining

“…To keep the statistics updated, the algorithm keeps a table with the observed attribute of each node. Each leaf saves the examples that have been observed so far (García-Martín et al, 2018).…”

“…∆ > , a node will replace that leaf, and the best feature will be split. That feature is abolished from the list of features which can be used to split that branch (García-Martín et al, 2018). If G ε ∆ < , it signifies that the highest and second-highest (.…”