Results 31 to 40 of about 6,745,293 (217)
Double Cost Sensitive Random Forest Algorithm
Journal of Harbin University of Science and Technology, 2021 A Double Cost Sensitive Random Forest (DCS-RF) algorithm is proposed to solve the problem that the accuracy of a few classes is not ideal when the classifier identifies unbalanced data.
ZHOU Yan-long, SUN Guang-lu
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Traffic Accident Severity Prediction Based on Random Forest
Sustainability, 2022 The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO)
Ming Yan, Yindong Shen
semanticscholar +1 more source
Feature-Weighting and Clustering Random Forest
International Journal of Computational Intelligence Systems, 2020 Classical random forest (RF) is suitable for the classification and regression tasks of high-dimensional data. However, the performance of RF may be not satisfied in case of few features, because univariate split method cannot bring more diverse ...
Zhenyu Liu +3 more
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Root zone soil moisture estimation with Random Forest
Journal of Hydrology, 2021 Accurate estimates of root zone soil moisture (RZSM) at relevant spatio-temporal scales are essential for many agricultural and hydrological applications.
C. Carranza +3 more
semanticscholar +1 more source
RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]
, 2020 Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang +5 more
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Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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The random forest algorithm for statistical learning
The Stata Journal, 2020 Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest.
Matthias Schonlau, Rosie Yuyan Zou
semanticscholar +1 more source
Random forest for gene selection and microarray data classification [PDF]
, 2011 A random forest method has been selected to perform both gene selection and classification of the microarray data. The goal of this research is to develop and improve the random forest gene selection method.
Moorthy, Kohbalan +1 more
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Portfolio Selection Using Random Forest Algorithm
International Journal of Computer Engineering and Data Science, 2022 Portfolio selection has long been a main topic in finance. What stocks should one invest in? How much should one allocate to each stock to maximize gain and minimize risk?
Daname KOLANI
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Sankhya A, 2018 Given an ensemble of randomized regression trees, it is possible to restructure them as a collection of multilayered neural networks with particular connection weights. Following this principle, we reformulate the random forest method of Breiman (2001) into a neural network setting, and in turn propose two new hybrid procedures that we call neural ...
Biau, Gérard +2 more
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