Results 11 to 20 of about 452,858 (310)

Random Forest

Journal of Insurance Medicine, 2017
For the task of analyzing survival data to derive risk factors associated with mortality, physicians, researchers, and biostatisticians have typically relied on certain types of regression techniques, most notably the Cox model.
Steven J. Rigatti
core   +3 more sources

Identifying Forest Fire Driving Factors and Related Impacts in China Using Random Forest Algorithm

Forests, 2020
Reasonable forest fire management measures can effectively reduce the losses caused by forest fires and forest fire driving factors and their impacts are important aspects that should be considered in forest fire management.
Wenyuan Ma, Zhongke Feng, Shilin Chen
exaly   +2 more sources

Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon

Forests, 2020
This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon.
Adam Waśniewski, Agata Hosciło, Bogdan Zagajewski   +2 more
exaly   +2 more sources

Improved random forest algorithms for increasing the accuracy of forest aboveground biomass estimation using Sentinel-2 imagery

Ecological Indicators
A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture,
Xiaoli Zhang, Hanwen Shen, Tianbao Huang, Yong Wu, Binbing Guo, Zhi Liu, Hongbin Luo, Jing Tang, Hang Zhou, Leiguang Wang, Weiheng Xu, Guanglong Ou   +11 more
doaj   +3 more sources

Reinforced random forest

Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, 2016
Reinforcement learning improves classification accuracy. But use of reinforcement learning is relatively unexplored in case of random forest classifier.
Dipti Prasad Mukherjee, Paul, Angshuman, Mukherjee, Dipti Prasad, Angshuman Paul   +3 more
core   +2 more sources

Improved Two-View Random Forest [PDF]

Jisuanji kexue yu tansuo, 2022
Random forest (RF) is one of the most classic machine learning methods, which has been widely used. However, although there are many two-view data in reality and extensive analytical research has been carried out, the RF construction for two-view ...
XIA Xiaoqiu, CHEN Songcan
doaj   +1 more source

Speaker Recognition using Random Forest [PDF]

ITM Web of Conferences, 2021
Speaker identification has become a mainstream technology in the field of machine learning that involves determining the identity of a speaker from his/her speech sample.
Khadar Nawas K, Kumar Barik Manish, Nayeemulla Khan A   +2 more
doaj   +1 more source

Random Forest for video Text Amazigh [PDF]

E3S Web of Conferences, 2021
In this paper; we introduce a system of automatic recognition of Video Text Amazigh based on the Random Forest. After doing some pretreatments on the video and picture, the text is segmented into lines and then into characters.
Rachidi Youssef
doaj   +1 more source

Random-Splitting Random Forest with Multiple Mixed-Data Covariates

Journal of Biostatistics and Epidemiology, 2023
Background: The bagging (BG) and random forest (RF) are famous supervised statistical learning methods based on classification and regression trees. The BG and RF can deal with different types of responses such as categorical, continuous, etc. There are
Mohammad Fayaz, Alireza Abadi, Soheila Khodakarimd   +2 more
doaj   +1 more source

HML-RF: Hybrid Multi-Label Random Forest

IEEE Access, 2022
Multi-label classification is the supervised learning problem in which an instance is associated with a set of labels. In this, labels are correlated, and hence label dependency information plays a vital role.
Vikas Jain, Ashish Phophalia, Suman K. Mitra   +2 more
doaj   +1 more source