FastForest: Increasing Random Forest Processing Speed While Maintaining Accuracy
This work addresses efficiency improvements for random forests, making them more suitable for smartphones and IoT devices, but it is incremental as it builds on existing methods.
The paper tackles the need for faster random forest algorithms on hardware-constrained devices by proposing FastForest, which achieves an average 24% increase in processing speed while maintaining or exceeding classification accuracy across 45 datasets.
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.