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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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Covariance regression with random forests [PDF]
BMC Bioinformatics, 2023 Capturing the conditional covariances or correlations among the elements of a multivariate response vector based on covariates is important to various fields including neuroscience, epidemiology and biomedicine.
Cansu Alakus +2 more
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Random forests, sound symbolism and Pokémon evolution. [PDF]
PLoS ONE, 2023 This study constructs machine learning algorithms that are trained to classify samples using sound symbolism, and then it reports on an experiment designed to measure their understanding against human participants.
Alexander James Kilpatrick +2 more
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Conditional variable importance for random forests [PDF]
BMC Bioinformatics, 2008 Background Random forests are becoming increasingly popular in many scientific fields because they can cope with "small n large p" problems, complex interactions and even highly correlated predictor variables.
Augustin Thomas +4 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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Functional random forests for curve response [PDF]
Scientific Reports, 2021 The rapid advancement of functional data in various application fields has increased the demand for advanced statistical approaches that can incorporate complex structures and nonlinear associations.
Guifang Fu, Xiaotian Dai, Yeheng Liang
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Phylogeny-informed random forests for human microbiome studies [PDF]
Microbiology SpectrumRandom Forest is a widely used tree-based ensemble learning algorithm that efficiently captures complex nonlinear relationships and higher-order feature interactions with no distributional assumptions to be satisfied.
Hyunwook Koh
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Posture-invariant myoelectric control with self-calibrating random forests [PDF]
Frontiers in NeuroroboticsIntroductionMyoelectric control systems translate different patterns of electromyographic (EMG) signals into the control commands of diverse human-machine interfaces via hand gesture recognition, enabling intuitive control of prosthesis and immersive ...
Xinyu Jiang +2 more
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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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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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