Large Random Forests: Optimisation for Rapid Evaluation

12/23/2019
by   Frederik Gossen, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset