Large Random Forests: Optimisation for Rapid Evaluation
This addresses efficiency issues for users of Random Forests in machine learning applications, offering a significant improvement but likely incremental as it builds on existing methods.
The paper tackles the problem of slow classification times in large Random Forests by aggregating them into a single decision diagram, achieving speed-ups of several orders of magnitude and reducing data structure size in experiments on popular datasets.
Random Forests are one of the most popular classifiers in machine learning. The larger they are, the more precise is the outcome of their predictions. However, this comes at a cost: their running time for classification grows linearly with the number of trees, i.e. the size of the forest. In this paper, we propose a method to aggregate large Random Forests into a single, semantically equivalent decision diagram. Our experiments on various popular datasets show speed-ups of several orders of magnitude, while, at the same time, also significantly reducing the size of the required data structure.