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Prediction of Heart Diseases using Random Forest
Journal of Physics: Conference Series, 2021 The process of discovering or mining information from a huge volume of data is known as data mining technology. Today data mining has lots of application in every aspects of human life. Applications of data mining are wide and diverse.
Madhumita Pal, S. Parija
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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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Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies [PDF]
, 2015 Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries ...
Barrett, Brian +5 more
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020 Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image ...
M. Sheykhmousa +5 more
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Random Forest variable importance with missing data [PDF]
, 2012 Random Forests are commonly applied for data prediction and interpretation. The latter purpose is supported by variable importance measures that rate the relevance of predictors. Yet existing measures can not be computed when data contains missing values.
Hapfelmeier, Alexander +2 more
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Three-Branch Random Forest Intrusion Detection Model
Mathematics, 2022 Network intrusion detection has the problems of large amounts of data, numerous attributes, and different levels of importance for each attribute in detection.
Chunying Zhang +4 more
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Robustness of Random Forest-based gene selection methods [PDF]
, 2013 Gene selection is an important part of microarray data analysis because it provides information that can lead to a better mechanistic understanding of an investigated phenomenon.
Kursa, Miron B.
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Journal of Thoracic Oncology, 2011 In the article by Chen et al,1 the authors used Random Survival Forests (RSF) as part of their approach for analyzing the data. In this note, we will explain RSF in a nontechnical way; precise details of the RSF method are described in the article by Ishwaran et al.2 RSF are an adaptation of Random Forests (RF)3 designed to be used for survival data ...
openaire +2 more sources
Review of Random Survival Forest method
Journal of Biostatistics and Epidemiology, 2020 Background: Over the past years, there has been a great deal of interest in applying statistical machine learning methods to survival analysis. Ensemble-based methods, especially random survival forest, have been developed in various fields, especially ...
Majid Rezaei +4 more
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Prediction of COVID-19 confirmed, death, and cured cases in India using random forest model
Big Data Mining and Analytics, 2021 A novel coronavirus (SARS-CoV-2) is an unusual viral pneumonia in patients, first found in late December 2019, latter it declared a pandemic by World Health Organizations because of its fatal effects on public health In this present, cases of COVID-19 ...
V. K. Gupta +3 more
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