Results 11 to 20 of about 67,103 (261)
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2021 AbstractsidClustering is a new random forests unsupervised machine learning algorithm. The first step in sidClustering involves what is called sidification of the features: staggering the features to have mutually exclusive ranges (called the staggered interaction data [SID] main features) and then forming all pairwise interactions (called the SID ...
Alejandro Mantero, Hemant Ishwaran
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Using random forests to model 90-day hometime in people with stroke
BMC Medical Research Methodology, 2021 Background Ninety-day hometime, the number of days a patient is living in the community in the first 90 after stroke, exhibits a non-normal bucket-shaped distribution, with lower and upper constraints making its analysis difficult.
Jessalyn K. Holodinsky +3 more
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Machine Learning, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Evaluation of random forests and Prophet for daily streamflow forecasting [PDF]
Advances in Geosciences, 2018 We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past ...
G. A. Papacharalampous +3 more
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Splitting on categorical predictors in random forests [PDF]
PeerJ, 2019 One reason for the widespread success of random forests (RFs) is their ability to analyze most datasets without preprocessing. For example, in contrast to many other statistical methods and machine learning approaches, no recoding such as dummy coding is
Marvin N. Wright, Inke R. König
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Segmentation of PMSE Data Using Random Forests
Remote Sensing, 2022 EISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes (PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of
Dorota Jozwicki +3 more
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Random Prism: An Alternative to Random Forests [PDF]
, 2011 Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach.
Stahl, F., Bramer, Max
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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 +2 more
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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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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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