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Journal of Combinatorial Theory, Series A, 1999 Suppose that rooted forests (in which the edges in each tree are directed away from the root of the tree) are formed by starting with a set of \(n\) labelled vertices and succesively adding an edge \(uv\) from a randomly chosen vertex \(u\) to the root \(v\) of a randomly chosen tree not containing \(u\). The author derives several enumeration formulae
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CoRR, 2017 This paper proposes a novel type of random forests called a denoising random forests that are robust against noises contained in test samples. Such noise-corrupted samples cause serious damage to the estimation performances of random forests, since unexpected child nodes are often selected and the leaf nodes that the input sample reaches are sometimes ...
Masaya Hibino +4 more
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, 2023 Forest fires are important factors that influence and restrict the development of forest ecosystems. In this paper, forest-fire-risk prediction was studied based on random forest (RF) and backpropagation neural network (BPNN) algorithms.
Chao Gao, Haiqing Hu, Honglei Lin
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On Oblique Random Forests [PDF]
, 2011 In his original paper on random forests, Breiman proposed two different decision tree ensembles: one generated from "orthogonal" trees with thresholds on individual features in every split, and one from "oblique" trees separating the feature space by randomly oriented hyperplanes.
Bjoern H. Menze +4 more
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Predecessors and successors in random mappings with exchangeable in-degrees [PDF]
, 2013 In this paper we characterise the distributions of the number of predecessors and of the number of successors of a given set of vertices, A, in the random mapping model, TnD^ (see Hansen and Jaworski (2008)), with exchangeable in-degree sequence (D^1,D^2,
Hansen, Jennie Charlotte +2 more
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CoRRWe propose a principled method for autoencoding with random forests. Our strategy builds on foundational results from nonparametric statistics and spectral graph theory to learn a low-dimensional embedding of the model that optimally represents relationships in the data. We provide exact and approximate solutions to the decoding problem via constrained
Binh Duc Vu +3 more
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CoRR, 2015 Random forests are a type of ensemble method which makes predictions by combining the results of several independent trees. However, the theory of random forests has long been outpaced by their application. In this paper, we propose a novel random forests algorithm based on cooperative game theory.
Jianyuan Sun +3 more
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The Macroeconomy as a Random Forest [PDF]
SSRN Electronic Journal, 2020 SummaryI develop the macroeconomic random forest (MRF), an algorithm adapting the canonical machine learning (ML) tool, to flexibly model evolving parameters in a linear macro equation. Its main output, generalized time‐varying parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, and ...
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A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.
PLoS ONE, 2014 Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus).
Joseph Mascaro +7 more
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An improved random forest algorithm for tracing the origin of metastatic renal cancer tissues
, 2023 Introduction Tracing the histological origin of metastatic renal cancer (MRC) and locating the pathological root cause lead to precise treatment and improved prognosis.
HaiDong Li, Tao Xie
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