Energy-aware very fast decision tree

Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm

Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of ...

Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree

Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and...

Extremely Fast Decision Tree

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree, that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of Hoeffding Anytime Tree—“Extremely Fast Decision Tree”, a minor modification to theMOA implementation of Hoeffding Tree—obtains significantly superior prequential accuracy onmost...

VERY FAST TREE - STRUCTURED VECTOR QUANTIZATIONTodd

Very Fast Tree-structured Vector Quantization

Very fast tree-structured vector quantization employs scalar quantization decisions at each level, but chooses the dimension on which to quantize based on the coordinate direction of maximum variance. Because the quantization is scalar, searches are no more complex than scalar quantization − providing significant improvement in complexity over full-searched or even tree-structured vector quanti...