Results 61 to 70 of about 5,516,106 (266)
Variable selection with Random Forests for missing data [PDF]
, 2013 Variable selection has been suggested for Random Forests to improve their efficiency of data prediction and interpretation. However, its basic element, i.e.
Hapfelmeier, Alexander, Ulm, Kurt
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Discriminating between glaucoma and normal eyes using optical coherence tomography and the 'Random Forests' classifier. [PDF]
PLoS ONE, 2014 To diagnose glaucoma based on spectral domain optical coherence tomography (SD-OCT) measurements using the 'Random Forests' method.SD-OCT was conducted in 126 eyes of 126 open angle glaucoma (OAG) patients and 84 eyes of 84 normal subjects.
Tatsuya Yoshida +6 more
doaj +1 more source
Modelling species presence-only data with random forests
bioRxiv, 2020 The Random Forest (RF) algorithm is an ensemble of classification or regression trees, and is a widely used and high-performing machine learning technique. It is increasingly used for species distribution modelling (SDM).
Roozbeh Valavi +3 more
semanticscholar +1 more source
Random Forests and Networks Analysis
, 2017 D. Wilson~\cite{[Wi]} in the 1990's described a simple and efficient algorithm based on loop-erased random walks to sample uniform spanning trees and more generally weighted trees or forests spanning a given graph. This algorithm provides a powerful tool
Avena, L. +3 more
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Enhancing random forests performance in microarray data classification [PDF]
, 2013 Random forests are receiving increasing attention for classification of microarray datasets. We evaluate the effects of a feature selection process on the performance of a random forest classifier as well as on the choice of two critical parameters, i.e.
DESSI, NICOLETTA +2 more
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A simple and effective approach to quantitatively characterize structural complexity
Scientific Reports, 2021 This study brings insight into interpreting forest structural diversity and explore the classification of individuals according to the distribution of the neighbours in natural forests. Natural forest communities with different latitudes and distribution
Gongqiao Zhang +3 more
doaj +1 more source
A random forest guided tour [PDF]
TEST, 2016 The random forest algorithm, proposed by L. Breiman in 2001, has been extremely successful as a general-purpose classification and regression method. The approach, which combines several randomized decision trees and aggregates their predictions by averaging, has shown excellent performance in settings where the number of variables is much larger than ...
Gérard Biau +2 more
openaire +5 more sources
Expert Systems with Applications, 2023 In machine learning, some models can make uncertain and imprecise predictions, they are called evidential models. These models may also be able to handle imperfect labeling and take into account labels that are richer than the commonly used hard labels, containing uncertainty and imprecision.
Arthur Hoarau +3 more
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, 2012 We consider unimodular random rooted trees (URTs) and invariant forests in Cayley graphs. We show that URTs of bounded degree are the same as the law of the component of the root in an invariant percolation on a regular tree.
Aldous +5 more
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, 2019 Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned.
Ge, S +4 more
openaire +3 more sources