Results 151 to 160 of about 6,745,293 (217)
Distinguishing a drug use disorder from drug use in a high-risk sample of youth: A random forest classification and explanatory analysis. [PDF]
Drug Alcohol Depend RepBerny LM, Guha A, Tanner-Smith EE.
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Machine Learning Modeling of Hospital Length of Stay After Breast Cancer Surgery: Comparison of Random Forest and Linear Regression Approaches. [PDF]
Medicina (Kaunas)Slavu I +6 more
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Optimized IoT protocol stack for seamless smart home communication using Random Forest-based interoperability analysis. [PDF]
Sci RepSriram A +3 more
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Journal of Insurance Medicine, 2017
For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
S. Rigatti
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For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model. With the advent of more widely distributed computing power, methods which require more complex mathematics have become ...
S. Rigatti
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Random forest in remote sensing: A review of applications and future directions
ISPRS Journal of Photogrammetry and Remote Sensing, 2016Mariana Belgiu, Lucian Dragut
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Accuracy and diversity-aware multi-objective approach for random forest construction
Expert systems with applications, 2023Random Forest is an ensemble classification approach. It aims to design a discrete finite group of decision trees constructed based on bootstrap samples and random attribute selection.
Nour El Islem Karabadji +5 more
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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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