Results 51 to 60 of about 6,745,293 (217)
Random forest swarm optimization-based for heart diseases diagnosis
Journal of Biomedical Informatics, 2021 Heart disease has been one of the leading causes of death worldwide in recent years. Among diagnostic methods for heart disease, angiography is one of the most common methods, but it is costly and has side effects.
S. Asadi, Seyed Ehsan Roshan, M. Kattan
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Journal of Spectroscopy, 2018 Hyperspectral datasets contain spectral noise, the presence of which adversely affects the classifier performance to generalize accurately. Despite machine learning algorithms being regarded as robust classifiers that generalize well under unfavourable ...
Na’eem Hoosen Agjee +3 more
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Improving random forest predictions in small datasets from two-phase sampling designs
BMC Medical Informatics and Decision Making, 2021 Background While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases—a common situation in ...
Sunwoo Han, B. Williamson, Y. Fong
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BarekengRandom forest algorithm allows for building better CART models. However, the disadvantage of this method is often underfitting, especially for small node sizes. Therefore, the double random forest method was developed to overcome this problem.
Arie Purwanto +2 more
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Investigation of the possibility of landslide hazard mapping using the Random Forest algorithm (Case study: Sardarabad Watershed, Lorestan Province) [PDF]
مخاطرات محیط طبیعی, 2018 With respect to the ability of data analysis techniques, their applications in various engineering and geosciences disciplines have been expanded. In this study, the random forest algorithm has been used for landslide susceptibility mapping in the ...
Ali Talebi +2 more
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Journal of Biostatistics and Epidemiology, 2021 Objective: Rapid technological advances in the last century and the large amount of information have made it difficult to analyze a large number of independent variables.
Maryam Deldar +2 more
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Random Forest Algorithm Based on Data Integration [PDF]
Jisuanji gongcheng, 2020 The historical data used for sales forecasting has the characteristics of sparseness and volatility,the traditional statistical or machine learning prediction algorithms for prediction perform poorly when the prediction cycle is long.Therefore,based on ...
XIE Kun, RONG Yutian, HU Fengping, CHEN Huan, YAO Xiaolong
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SMOTE and Weighted Random Forest for Classification of Areas Based on Health Problems in Java
Journal of Applied Informatics and ComputingRandom Forest (RF) is a popular Machine Learning (ML) approach extensively employed for addressing classification issues. Nevertheless, the RF method for classification problems demonstrates suboptimal performance in cases of data imbalance.
Erwan Setiawan +2 more
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Weather prediction using random forest machine learning model
Indonesian Journal of Electrical Engineering and Computer Science, 2021 The complex numerical climate models pose a big challenge for scientists in weather predictions, especially for tropical system. This paper is focused on presenting the importance of weather prediction using machine learning (ML) technique. Recently many
R. Meenal +3 more
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A Multi-Task Framework for Action Prediction
Information, 2020 Predicting the categories of actions in partially observed videos is a challenging task in the computer vision field. The temporal progress of an ongoing action is of great importance for action prediction, since actions can present different ...
Tianyu Yu +3 more
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