Stochastic Aware Random Forests - A Variation Less Impacted by Randomness

The impact of random choices is important to many ensemble classifiers algorithms, and the Random Forests is particularly sensible to pseudo-random number generation decisions.This paper proposes an extension to the classical Random Forests method that aims to reduce its sensibility to randomness.The benefits brought by such extension are illustrated by a large number of experiments over 32 different public data sets.