Stochastic Aware Random Forests - A Variation Less Impacted by Randomness.
Paulo Fernandes, Lucelene Lopes, Silvio Normey, Duncan D. Ruiz · 2013
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. The effectiveness of ensemble classifiers for classification tasks in the machine learning area is a known fact. Classical methods as Bagging (Breiman 1996) and Random Forests (Breiman 2001) are widely spread in both researchers and practitioners communities. However, all ensemble classifiers rely on pseudo-random choices to generate