Results 21 to 30 of about 6,285,017 (364)

Traffic Accident Severity Prediction Based on Random Forest

Sustainability, 2022
The prediction of traffic accident severity is essential for traffic safety management and control. To achieve high prediction accuracy and model interpretability, we propose a hybrid model that integrates random forest (RF) and Bayesian optimization (BO)
Ming Yan, Yindong Shen
semanticscholar   +1 more source

Feature-Weighting and Clustering Random Forest

International Journal of Computational Intelligence Systems, 2020
Classical random forest (RF) is suitable for the classification and regression tasks of high-dimensional data. However, the performance of RF may be not satisfied in case of few features, because univariate split method cannot bring more diverse ...
Zhenyu Liu, Tao Wen, Wei Sun, Qilong Zhang   +3 more
doaj   +1 more source

RFDCR:Automated brain lesion segmentation using cascaded random forests with dense conditional random fields [PDF]

, 2020
Segmentation of brain lesions from magnetic resonance images (MRI) is an important step for disease diagnosis, surgical planning, radiotherapy and chemotherapy.
Chen, Gaoxiang, Li, Qun, Pan, Zhifang, Rekik, Islem, Shi, Fuqian, Wang, Li   +5 more
core   +2 more sources

A Comprehensive Study of Random Forest for Short-Term Load Forecasting

Energies, 2022
Random forest (RF) is one of the most popular machine learning (ML) models used for both classification and regression problems. As an ensemble model, it demonstrates high predictive accuracy and low variance, while being easy to learn and optimize.
Grzegorz Dudek
semanticscholar   +1 more source

The random forest algorithm for statistical learning

The Stata Journal, 2020
Random forests (Breiman, 2001, Machine Learning 45: 5–32) is a statistical- or machine-learning algorithm for prediction. In this article, we introduce a corresponding new command, rforest.
Matthias Schonlau, Rosie Yuyan Zou
semanticscholar   +1 more source

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, Diya Sun, Peixin Li, Tae-Kyun Kim, Tianmin Xu, Yuru Pei   +5 more
doaj   +1 more source

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
doaj   +1 more source

Random Forests [PDF]

Machine Learning, 2001
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

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.
core   +4 more sources

Neural Random Forests

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, Scornet, Erwan, Welbl, Johannes   +2 more
openaire   +3 more sources