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Random Forests: elements of theory, variable selection and applications

, 2010
Robin Genuer
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On learning Random Forests for Random Forest-clustering

2020 25th International Conference on Pattern Recognition (ICPR), 2021
In this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context.
M. Bicego, Francisco Escolano
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Random forests

Data Analytics for the Social Sciences, 2021
Carlos Becker
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FARF: A Fair and Adaptive Random Forests Classifier

Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2021
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning algorithms focus on the offline setting.
Wenbin Zhang, A. Bifet, Xiangliang Zhang, Jeremy C. Weiss, W. Nejdl   +4 more
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MDA for random forests: inconsistency, and a practical solution via the Sobol-MDA

Biometrika, 2021
Variable importance measures are the main tools used to analyse the black-box mechanisms of random forests. Although the mean decrease accuracy is widely accepted as the most efficient variable importance measure for random forests, little is known ...
Cl'ement B'enard, S. Veiga, Erwan Scornet   +2 more
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Entropy and Confidence-Based Undersampling Boosting Random Forests for Imbalanced Problems

IEEE Transactions on Neural Networks and Learning Systems, 2020
In this article, we propose a novel entropy and confidence-based undersampling boosting (ECUBoost) framework to solve imbalanced problems. The boosting-based ensemble is combined with a new undersampling method to improve the generalization performance ...
Zhe Wang, Chenjie Cao, Yujin Zhu
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Estimation of the dynamic modulus of asphalt concretes using random forests algorithm

International Journal of Pavement Engineering, 2020
Dynamic modulus ( ) of asphalt can be estimated using predictive models to avoid the time-taking and costly laboratory-based measurements. Several predictive models such as the Witczak model have been widely used by many researchers for the prediction of
D. Daneshvar, A. Behnood
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Random Forest

Journal of Insurance Medicine, 2017
For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
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Components of Random Forests

Combinatorics, Probability and Computing, 1992
A forest ℱ(n, M) chosen uniformly from the family of all labelled unrooted forests with n vertices and M edges is studied. We show that, like the Érdős-Rényi random graph G(n, M), the random forest exhibits three modes of asymptotic behaviour: subcritical, nearcritical and supercritical, with the phase transition at the point M = n/2.
Boris Pittel, Tomasz Łuczak
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Random recursive forests

Random Structures & Algorithms, 1994
AbstractA random recursive forest is defined as a union of random recursive trees. We find the expected number of trees in the uniform random recursive forest as well as the number of vertices of given degree, the maximum degree, the height of vertices, the order of branches, the root of the component containing a given vertex, and the last root of ...
Krystyna T. Balińska, Jerzy Szymanski, Louis V. Quintas   +2 more
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