On PAC-Bayesian bounds for random forests
Sign Up to like & getrecommendations! Published in 2019 at "Machine Learning"
DOI: 10.1007/s10994-019-05803-4
Abstract: Existing guarantees in terms of rigorous upper bounds on the generalization error for the original random forest algorithm, one of the most frequently used machine learning methods, are unsatisfying. We discuss and evaluate various PAC-Bayesian… read more here.
Keywords: bayesian bounds; bounds random; random forests; taking majority ... See more keywords
Multiple Random Forests Modelling for Urban Water Consumption Forecasting
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DOI: 10.1007/s11269-017-1774-7
Abstract: The precise forecasting of water consumption is the basis in water resources planning and management. However, predicting water consumption fluctuations is complicated, given their non-stationary and non-linear characteristics. In this paper, a multiple random forests… read more here.
Keywords: water; water consumption; random forests; urban water ... See more keywords
Mining Big Data with Random Forests
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DOI: 10.1007/s12559-018-9615-4
Abstract: In the current big data era, naive implementations of well-known learning algorithms cannot efficiently and effectively deal with large datasets. Random forests (RFs) are a popular ensemble-based method for classification. RFs have been shown to… read more here.
Keywords: big data; mining big; random forests; large datasets ... See more keywords
Evaluation of random forests and generalized regression neural networks for daily reference evapotranspiration modelling.
Sign Up to like & getrecommendations! Published in 2017 at "Agricultural Water Management"
DOI: 10.1016/j.agwat.2017.08.003
Abstract: Abstract Accurate estimation of reference evapotranspiration (ET 0 ) is of importance for regional water resource management. The present study proposed two artificial intelligence models, random forests (RF) and generalized regression neural networks (GRNN), for… read more here.
Keywords: regression neural; neural networks; random forests; generalized regression ... See more keywords
Improvement of rainfall estimation from MSG data using Random Forests classification and regression
Sign Up to like & getrecommendations! Published in 2018 at "Atmospheric Research"
DOI: 10.1016/j.atmosres.2018.05.001
Abstract: Abstract In this study, a new rainfall estimation technique on 3 h and 24 h scales applied in Northern Algeria is presented. The proposed technique is based on Random Forests (RF) algorithm using data retrieved from Meteosat… read more here.
Keywords: classification; regression; rainfall estimation; random forests ... See more keywords
A hospital wide predictive model for unplanned readmission using hierarchical ICD data
Sign Up to like & getrecommendations! Published in 2019 at "Computer methods and programs in biomedicine"
DOI: 10.1016/j.cmpb.2019.02.007
Abstract: BACKGROUND AND OBJECTIVE Hospitals already acquire a large amount of data, mainly for administrative, billing and registration purposes. Tapping on these already available data for additional purposes, aiming at improving care, without significant incremental effort… read more here.
Keywords: hospital wide; pathology; random forests; model ... See more keywords
Delamination detection in composite plates using random forests
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DOI: 10.1016/j.compstruct.2021.114676
Abstract: Abstract Laminated composites remain an important family of advanced materials playing essential roles in the development of high-performance components. A classic challenge with this composite family is delamination. Lately, vibration-based methods augmented by dynamic response… read more here.
Keywords: delamination; delamination composite; random forests; composite plates ... See more keywords
Use of random forests regression for predicting IRI of asphalt pavements
Sign Up to like & getrecommendations! Published in 2018 at "Construction and Building Materials"
DOI: 10.1016/j.conbuildmat.2018.09.017
Abstract: Abstract Random forest is a powerful machine learning algorithm with demonstrated success. In this study, the authors developed a random forests regression (RFR) model to estimate the international roughness index (IRI) of flexible pavements from… read more here.
Keywords: regression; random forests; model; iri ... See more keywords
Data-driven switching modeling for MPC using Regression Trees and Random Forests
Sign Up to like & getrecommendations! Published in 2020 at "Nonlinear Analysis: Hybrid Systems"
DOI: 10.1016/j.nahs.2020.100882
Abstract: Abstract Model Predictive Control is a well consolidated technique to design optimal control strategies, leveraging the capability of a mathematical model to predict a system’s behavior over a time horizon. However, building physics-based models for… read more here.
Keywords: methodology; data driven; random forests; regression trees ... See more keywords
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
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DOI: 10.1016/j.neucom.2016.05.082
Abstract: Automatic labeling of the hippocampus in brain MR images is highly demanded, as it has played an important role in imaging-based brain studies. However, accurate labeling of the hippocampus is still challenging, partially due to… read more here.
Keywords: brain images; hippocampus; random forests; spatially localized ... See more keywords
Lithological mapping in the Central African Copper Belt using Random Forests and clustering: Strategies for optimised results
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DOI: 10.1016/j.oregeorev.2019.103015
Abstract: Abstract The Trident project is located in the Domes region of the Central African Copper Belt and hosts a number of mineralised systems including the Sentinel (Ni) and Enterprise (Cu) deposits. The project has received… read more here.
Keywords: african copper; lithology; geology; copper belt ... See more keywords