AAAI Publications, The Twenty-Sixth International FLAIRS Conference

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Stochastic Aware Random Forests - A Variation Less Impacted by Randomness
Paulo Fernandes, Lucelene Lopes, Silvio Normey, Duncan Ruiz

Last modified: 2013-05-19

Abstract


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.

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