Results 161 to 170 of about 6,745,293 (217)

Multinomial random forest

Pattern Recognition, 2022
Despite the impressive performance of random forests (RF), its theoretical properties have not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed multinomial random forest (MRF), to analyze its consistency and privacy ...
Jiawang Bai, Yiming Li, Jiawei Li, Xue Yang, Yong Jiang, Shutao Xia   +5 more
semanticscholar   +1 more source

Large group activity security risk assessment and risk early warning based on random forest algorithm

Pattern Recognition Letters, 2021
With the continuous development of artificial intelligence, machine learning, the necessary way to achieve artificial intelligence, is also constantly improving, of which deep learning is one of the contents.
Yanyu Chen, Wenzhe Zheng, Wenbo Li, Yimiao Huang   +3 more
semanticscholar   +1 more source

Predictive modeling of groundwater nitrate pollution and evaluating its main impact factors using random forest.

Chemosphere, 2021
Groundwater quality in plains and basins of arid and semi-arid regions with increased agriculture and urbanization development faces severe nitrate pollution, which is affected by both climate and anthropogenic activities.
Song He, Jianhua Wu, Dan Wang, Xiaodong He   +3 more
semanticscholar   +1 more source

Mapping Forest Height and Aboveground Biomass by Integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 Data Using Random Forest Algorithm in Northwest Himalayan Foothills of India

Geophysical Research Letters, 2021
The present study aims to map forest canopy height by integrating ICESat‐2 and Sentinel‐1 data and investigate the effect of integrating forest canopy height information with Sentinel‐2 data‐derived spectral variables on the prediction of spatial ...
S. Nandy, Ritika Srinet, H. Padalia
semanticscholar   +1 more source

A comparison of random forest based algorithms: random credal random forest versus oblique random forest

Soft Computing, 2018
Random forest (RF) is an ensemble learning method, and it is considered a reference due to its excellent performance. Several improvements in RF have been published. A kind of improvement for the RF algorithm is based on the use of multivariate decision trees with local optimization process (oblique RF).
Carlos J. Mantas, Javier G. Castellano, Serafín Moral-García, Joaquín Abellán   +3 more
openaire   +1 more source

Random Forest Algorithm Overview

Babylonian Journal of Machine Learning
A random forest is a machine learning model utilized in classification and forecasting. To train machine learning algorithms and artificial intelligence models, it is crucial to have a substantial amount of high-quality data for effective data collecting.
Hasan Ahmed Salman, Ali Kalakech, Amani Steiti   +2 more
semanticscholar   +1 more source

A random forest model of landslide susceptibility mapping based on hyperparameter optimization using Bayes algorithm

Geomorphology, 2020
The choice of model parameters in landslide susceptibility mapping makes a major determinant of model accuracy. The purpose of this study is to optimize the hyperparameters based on a Bayesian optimization algorithm, and to obtain a high accuracy random ...
Deliang Sun, Haijia Wen, Danzhou Wang, Jiahui Xu   +3 more
semanticscholar   +1 more source

On learning Random Forests for Random Forest-clustering

2020 25th International Conference on Pattern Recognition (ICPR), 2021
In this paper we study the poorly investigated problem of learning Random Forests for distance-based Random Forest clustering. We studied both classic schemes as well as alternative approaches, novel in this context. In particular, we investigated the suitability of Gaussian Density Forests [1], Random Forests specifically designed for density ...
Bicego, M, Escolano, F
openaire   +2 more sources

Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods.

Science of the Total Environment, 2019
Floods are one of the most devastating types of disasters that cause loss of lives and property worldwide each year. This study aimed to evaluate and compare the prediction capability of the naïve Bayes tree (NBTree), alternating decision tree (ADTree ...
Wei Chen, Yang Li, Weifeng Xue, H. Shahabi, Shao-jun Li, H. Hong, Xiaojing Wang, Huiyuan Bian, Shuai Zhang, B. Pradhan, B. Ahmad   +10 more
semanticscholar   +1 more source

An improved random forest based on the classification accuracy and correlation measurement of decision trees

Expert systems with applications, 2023
Zhigang Sun, Guotao Wang, Pengfei Li, Hui Wang, Min Zhang, Xiaowen Liang   +5 more
semanticscholar   +1 more source