Results 11 to 20 of about 13,565 (211)
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 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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Classification of shale gas “sweet spot” based on Random Forest machine learning [PDF]
Youqicang pingjia yu kaifa, 2023 The classification and identification of shale gas “sweet spot” involves a variety of different factors, which requires personal experience, and is usually time and resources consuming.
NIE Yunli, GAO Guozhong
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A two-stage random forest-based pathway analysis method. [PDF]
PLoS ONE, 2012 Pathway analysis provides a powerful approach for identifying the joint effect of genes grouped into biologically-based pathways on disease. Pathway analysis is also an attractive approach for a secondary analysis of genome-wide association study (GWAS ...
Ren-Hua Chung, Ying-Erh Chen
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Implementation of LightGBM and Random Forest in Potential Customer Classification
TIERS Information Technology Journal, 2023 Classification is one of the data mining techniques that can be used to determine potential custumers. Previous research show that the boosting method, especially LGBM, produces the highest accuracy value of all models, namely 100%.
Laura Sari +3 more
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An Approximation Method for Fitted Random Forests
CoRR, 2022 Random Forests (RF) is a popular machine learning method for classification and regression problems. It involves a bagging application to decision tree models. One of the primary advantages of the Random Forests model is the reduction in the variance of the forecast.
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Forest-RK: A New Random Forest Induction Method [PDF]
, 2008 In this paper we present our work on the parametrization of Random Forests (RF), and more particularly on the number K of features randomly selected at each node during the tree induction process. It has been shown that this hyperparameter can play a significant role on performance.
Simon Bernard 0001 +2 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.
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Method for Profile-Guided Optimization of Android Applications Using Random Forest
IEEE Access, 2022 When choosing a smartphone, many users are guided by the performance of smartphones and the speed of applications. Because it is difficult to measure the application speed directly, the speed of the application startup is considered and used for ...
Andrei Visochan +7 more
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Predicting ATP-Binding Cassette Transporters Using the Random Forest Method
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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