  Random Forest remains one of Data Mining's most enduring ensemble algorithms,
achieving well-documented levels of accuracy and processing speed, as well as
regularly appearing in new research. However, with data mining now reaching the
domain of hardware-constrained devices such as smartphones and Internet of
Things (IoT) devices, there is continued need for further research into
algorithm efficiency to deliver greater processing speed without sacrificing
accuracy. Our proposed FastForest algorithm delivers an average 24% increase in
processing speed compared with Random Forest whilst maintaining (and frequently
exceeding) it on classification accuracy over tests involving 45 datasets.
FastForest achieves this result through a combination of three optimising
components - Subsample Aggregating ('Subbagging'), Logarithmic Split-Point
Sampling and Dynamic Restricted Subspacing. Moreover, detailed testing of
Subbagging sizes has found an optimal scalar delivering a positive mix of
processing performance and accuracy.
