Results 41 to 50 of about 6,745,293 (217)
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 forest-based prediction of stroke outcome [PDF]
Scientific Reports, 2021 We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3-months after admission.
C. Fernandez-Lozano +11 more
semanticscholar +1 more source
rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning [PDF]
, 2014 In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of only binary ones.
Kursa, Miron B.
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Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies [PDF]
, 2015 Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries ...
Barrett, Brian +5 more
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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Robustness of Random Forest-based gene selection methods [PDF]
, 2013 Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon.
Kursa, Miron B.
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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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Fecal source identification using random forest
Microbiome, 2018 Background Clostridiales and Bacteroidales are uniquely adapted to the gut environment and have co-evolved with their hosts resulting in convergent microbiome patterns within mammalian species.
Adélaïde Roguet +3 more
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Pilar Nusa Mandiri, 2022 This study predicts and classifies benign and malignant breast cancer using 3 classification models. The method used in this research is Random Forest, Naïve Bayes and AdaBoost.
Bahtiar Imran +5 more
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Crop Yield Prediction Using Improved Random Forest [PDF]
ITM Web of Conferences, 2023 Agriculture has an important role in India’s economic development. Crop productivity is affected by the rising population and the country’s ever-changing climate. Crop yield estimation is a challenge in the farming sector.
T. Padma, Sinha Dipali
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