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Predicting Metabolic Syndrome Using the Random Forest Method [PDF]
The Scientific World Journal, 2015 Aims. This study proposes a computational method for determining the prevalence of metabolic syndrome (MS) and to predict its occurrence using the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria. The Random Forest
Apilak Worachartcheewan +5 more
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Random forest method for estimation of brake specific fuel consumption [PDF]
Scientific Reports, 2023 The internal combustion engine is a widely used power equipment in various fields, and its energy utilization is measured using brake specific fuel consumption (BSFC).
Qinsheng Yun +3 more
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Random forest-based track initiation method [PDF]
The Journal of Engineering, 2019 In this study, a novel method based on the random forest is presented to solve the problem of track initiation in the air-traffic-control (ATC) radar system. ATC radar is the most common civilian surveillance radar. There are dense targets with different
Shuo Liu +4 more
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Predicting ATP-Binding Cassette Transporters Using the Random Forest Method [PDF]
Frontiers in Genetics, 2020 ATP-binding cassette (ABC) proteins play important roles in a wide variety of species. These proteins are involved in absorbing nutrients, exporting toxic substances, and regulating potassium channels, and they contribute to drug resistance in cancer ...
Ruiyan Hou +3 more
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Review of Random Survival Forest method
Journal of Biostatistics and Epidemiology, 2020 Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially ...
Majid Rezaei +4 more
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Random Forests and Kernel Methods [PDF]
IEEE Transactions on Information Theory, 2016 Random forests are ensemble methods which grow trees as base learners and combine their predictions by averaging. Random forests are known for their good practical performance, particularly in high dimensional set-tings. On the theoretical side, several studies highlight the potentially fruitful connection between random forests and kernel methods.
Erwan Scornet
openaire +6 more sources
Source discrimination of mine water based on the random forest method [PDF]
Scientific Reports, 2022 Machine learning is one of the widely used techniques to pattern recognition. Use of the machine learning tools is becoming a more accessible approach for predictive model development in preventing engineering disaster.
Zhenwei Yang +3 more
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BiMM forest: A random forest method for modeling clustered and longitudinal binary outcomes. [PDF]
Chemometr Intell Lab Syst, 2019 Clustered binary outcomes and datasets with many predictor variables are frequently encountered in clinical research (e.g. longitudinal studies). Generalized linear mixed models (GLMMs) typically employed for clustered endpoints have challenges for some scenarios, particularly for complex datasets which contain many interactions among predictors and ...
Speiser JL +5 more
europepmc +4 more sources
Evaluation of liquefaction potential in central Taiwan using random forest method [PDF]
Scientific ReportsLiquefaction is a significant geotechnical hazard in seismically active regions like Taiwan, threatening infrastructure and public safety. Accurate prediction models are essential for assessing soil susceptibility to liquefaction during seismic events ...
Chih-Yu Liu +3 more
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Computational and Structural Biotechnology Journal, 2022 Gene-to-gene networks, such as Gene Regulatory Networks (GRN) and Predictive Expression Networks (PEN) capture relationships between genes and are beneficial for use in downstream biological analyses. There exists multiple network inference tools to produce these gene-to-gene networks from matrices of gene expression data.
Walker, Angelica M. +7 more
openaire +7 more sources