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Soft Computing, 2018
Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Serafín Moral-García +3 more
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Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Serafín Moral-García +3 more
openaire +2 more sources
iForest: Interpreting Random Forests via Visual Analytics
IEEE Transactions on Visualization and Computer Graphics, 2019As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs.
Xun Zhao, Yanhong Wu, Lee, Weiwei Cui
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Optimizing random forests: spark implementations of random genetic forests
BOHR International Journal of Engineering, 2022The Random Forest (RF) algorithm, originally proposed by Breiman et al. (1), is a widely used machine learning algorithm that gains its merit from its fast learning speed as well as high classification accuracy. However, despiteits widespread use, the different mechanisms at work in Breiman’s RF are not yet fully understood, and there is stillon-going ...
Sikha Bagui, Timothy Bennett
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Decision trees and random forests.
American Journal of Orthodontics and Dentofacial Orthopedics, 2023Thijs Becker +4 more
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IEEE Transactions on Medical Imaging, 2018
Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning.
Chao Ma, Gongning Luo, Kuanquan Wang
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Segmentation of brain tumors from magnetic resonance imaging (MRI) data sets is of great importance for improved diagnosis, growth rate prediction, and treatment planning.
Chao Ma, Gongning Luo, Kuanquan Wang
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2019 Scientific Meeting on Electrical-Electronics & Biomedical Engineering and Computer Science (EBBT), 2019
In this study, a novel method is proposed for the detection of Parkinson's disease with the features obtained from the speech signals. Detection and early diagnosis of Parkinson's disease are essential in terms of disease progression and treatment ...
K. Polat
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In this study, a novel method is proposed for the detection of Parkinson's disease with the features obtained from the speech signals. Detection and early diagnosis of Parkinson's disease are essential in terms of disease progression and treatment ...
K. Polat
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Random forests for global sensitivity analysis: A selective review
Reliability Engineering & System Safety, 2021A. Antoniadis +2 more
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Random Forest for the Real Forests
2015A forest is a vast area of land covered predominantly with trees and undergrowth. In this paper, adhering to cartographic variables, we try to predict the predominant kind of tree cover of a forest using the Random Forests (RF) classification method. The study classifies the data into seven classes of forests found in the Roosevelt National Forest of ...
Tanvir Ahmad +2 more
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[23] Random Forests for Microarrays
2006Random Forests is a powerful multipurpose tool for predicting and understanding data. If gene expression data come from known groups or classes (e.g., tumor patients and controls), Random Forests can rank the genes in terms of their usefulness in separating the groups.
John R. Stevens, Adele Cutler
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