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Pruning Random Forest with Orthogonal Matching Trees

Abstract : In this paper we propose a new method to reduce the size of Breiman's Random Forests. Given a Random Forest and a target size, our algorithm builds a linear combination of trees which minimizes the training error. Selected trees, as well as weights of the linear combination are obtained by mean of the Orthogonal Matching Pursuit algorithm. We test our method on many public benchmark datasets both on regression and binary classification and we compare it to other pruning techniques. Experiments show that our technique performs significantly better or equally good on many datasets 1. We also discuss the benefit and shortcoming of learning weights for the pruned forest which lead us to propose to use a non-negative constraint on the OMP weights for better empirical results.
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Submitted on : Wednesday, April 15, 2020 - 3:19:47 PM
Last modification on : Wednesday, November 3, 2021 - 7:31:31 AM


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  • HAL Id : hal-02534421, version 1


Luc Giffon, Charly Lamothe, Léo Bouscarrat, Paolo Milanesi, Farah Cherfaoui, et al.. Pruning Random Forest with Orthogonal Matching Trees. 2020. ⟨hal-02534421⟩



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