Random forest(RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to ...
Aiming to address the problem of strong randomness and strong temporal correlations in wind power prediction (WPP), a new framework for WPP based on RF-WOA-VMD and BiGRU optimized by an attention mechanism is proposed. Firstly, the random forest algorithm (RF) is adopted to screen the ...
the model factors should be selected comprehensively. The random forest (RF) algorithm is a suitable for handling the selection and classification work and is commonly used in different fields37,38,39. In this study, the RF model is built to screen out the core factors. ...
heterogeneity of the dataset, such as the lack of data from lower administrative units in the country. In such cases, the predictive ML algorithm can be updated and re-trained in the future when the reliable data is added. 本期编辑 帅晨阳,密西根大学环境与可持续性学院,博士研究生,研究方向:D...
Kernel Principal Component Analysis (KPCA), Support Vector Machine (SVM), Random Forest (RF), Artificial Neural Network (ANN). Full size image Figure 5 (a,b) shows the data with a p-value for the comparison of three machine learning models for sensitivity and specificity, respectively. ...
Based on the random forest (RF) algorithm, the static compressive strength of rocks was estimated based on the available drilling data, such as weight on bit, drill string rotating speed, drilling torque, stand-pipe pressure, mud pumping rate, and penetration rate (Gamal et al., 2021). ...
Nevertheless, a possible limitation of RF is that it generates a forest consisting of many trees and rules, thus it is viewed as a black box model. In this paper, the RF+HC methods for rule extraction from RF are proposed. Once the RF is built, a hill climbing algorithm is used to ...
Mashayekhi, M. and Gras, R. (2015) Rule Extraction from Random Forest: The RF + HC Methods. Advances in Artificial Intelligence, Canberra, 30 November-4 De- cember 2015, 223-237.M. Mashayekhi, R. Gras, Rule extraction from random forest: The RF+HC methods, in: Proceedings of the ...
For the prediction of RS properties (continuous type features), we used linear model (LM), artificial neural network (ANN), and random forest (RF) algorithms. The categorical type features were predicted by a DT algorithm. Moreover, the feature importance of continuous and categorical features ...
此文档采用R中的mlr包中的smote算法来处理数据类别不平衡的问题,用Microsoft R Server(专业版R)中的RevoScaleR包中rxFastForest函数进行随机森林建模。采用mlr包调用randomforest包的randomForest函数建模,进行并行运算,效率依然低下,不能满足正常工作;因此需要调用RevoScaleR包的函数,rxDForest可以进行随机森林建模,但是效率...