Results 11 to 20 of about 982,398 (277)
Unsupervised random forest for affinity estimation
Computational Visual Media, 2021 This paper presents an unsupervised clustering random-forest-based metric for affinity estimation in large and high-dimensional data. The criterion used for node splitting during forest construction can handle rank-deficiency when measuring cluster ...
Yunai Yi +5 more
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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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rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning [PDF]
, 2014 In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of only binary ones.
Kursa, Miron B.
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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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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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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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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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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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Archives of Medical Science, 2021 Introduction It is essential to predict the survival status of patients based on their prognosis. This can assist physicians in evaluating treatment decisions. Random forest is an excellent machine learning algorithm even without any modification.
Cheng Xu +4 more
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