Results 11 to 20 of about 5,516,106 (266)
Random Forests for Big Data [PDF]
Big Data Research, 2017 Big Data is one of the major challenges of statistical science and has numerous consequences from algorithmic and theoretical viewpoints. Big Data always involve massive data but they also often include online data and data heterogeneity. Recently some statistical methods have been adapted to process Big Data, like linear regression models, clustering ...
Villa-Vialaneix, Nathalie +3 more
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Consistency of random forests [PDF]
The Annals of Statistics, 2015 Random forests are a learning algorithm proposed by Breiman [Mach. Learn. 45 (2001) 5--32] that combines several randomized decision trees and aggregates their predictions by averaging. Despite its wide usage and outstanding practical performance, little is known about the mathematical properties of the procedure.
Scornet, Erwan +2 more
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Random Prism: An Alternative to Random Forests [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach.
Stahl, F., Bramer, Max
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, 2008 Leo Breimans Random Forests (RF) is a recent development in tree based classifiers and quickly proven to be one of the most important algorithms in the machine learning literature. It has shown robust and improved results of classifications on standard data sets. Ensemble learning algorithms such as AdaBoost and Bagging have been in active research and
Praveen Boinee +2 more
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Random KNN feature selection - a fast and stable alternative to Random Forests [PDF]
BMC Bioinformatics, 2011 Background Successfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available.
Li Shengqiao +2 more
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, 2020 This book offers an application-oriented guide to random forests: a statistical learning method extensively used in many fields of application, thanks to its excellent predictive performance, but also to its flexibility, which places few restrictions on the nature of the data used. Indeed, random forests can be adapted to both supervised classification
R. Genuer, Jean‐Michel Poggi
semanticscholar +3 more sources
On random trees and forests [PDF]
ESAIM: Proceedings and Surveys, 2023 The first talk at the session Random trees and random forests “Journée MAS” (27/08/2021) was presented by I. Kortchemski. After a general up-to-date introduction to local and scaling limits of Bienaymé trees (which are discrete branching trees), he ...
Contat Alice +4 more
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IEEE Access, 2022 Random forests of axis-parallel decision trees still show competitive accuracy in various tasks; however, they have drawbacks that limit their applicability. Namely, they perform poorly for multidimensional sparse data.
Dmitry A. Devyatkin, Oleg G. Grigoriev
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Random Forests for Time Series
Revstat Statistical Journal, 2023 Random forests are a powerful learning algorithm. However, when dealing with time series, the time-dependent structure is lost, assuming the observations are independent. We propose some variants of random forests for time series. The idea is to replace
Benjamin Goehry +4 more
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Tracking Cloud Forests With Cloud Technology and Random Forests
Frontiers in Environmental Science, 2021 Hotspots of endemic biodiversity, tropical cloud forests teem with ecosystem services such as drinking water, food, building materials, and carbon sequestration. Unfortunately, already threatened by climate change, the cloud forests in our study area are
Pasky Pascual, Cam Pascual
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