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.