Hence, the proposed model demonstrates superior predictive power over other benchmark models.Īrtificial intelligence COVID-19 broad learning system (BLS) coronavirus disease 2019 (COVID-19) testing capacity random forest (RF) time-series forecasting. Classication trees are adaptive and robust, but do not generalize well. In addition, we compared the forecasting results with linear regression (LR) model, -nearest neighbors (KNN), decision tree (DT), adaptive boosting (Ada), RF, gradient boosting DT (GBDT), support vector regression (SVR), extra trees (ETs) regressor, CatBoost (CAT), LightGBM (LGB), XGBoost (XGB), and BLS.The RF-Bagging BLS model showed better forecasting performance in terms of relative mean-square error (RMSE), coefficient of determination (), adjusted coefficient of determination (), median absolute error (MAD), and mean absolute percentage error (MAPE) than other models. Trees, Bagging, Random Forests and Boosting Classication Trees Bagging: Averaging Trees Random Forests: Cleverer Averaging of Trees Boosting: Cleverest Averaging of Trees Methods for improving the performance of weak learners such as Trees. Then, we combine the bagging strategy and BLS to develop a random-forest-bagging BLS (RF-Bagging-BLS) approach to forecast the trend of the COVID-19 pandemic. Here, we leveraged random forest (RF) to screen out the key features. Random forest is a way of averaging multiple deep decision trees, trained on different parts of the same training set, with the goal of overcoming over-fitting. RF classifier is an ensemble method that trains several decision trees in parallel with bootstrapping followed by aggregation, jointly referred as bagging. Also, overfitting does not occur in the random forest but in Gradient boosting algorithms due to adding several new trees. We proposed a machine learning model for COVID-19 prediction based on the broad learning system (BLS). In this work, a large COVID-19 data set consisting of COVID-19 pandemic, COVID-19 testing capacity, economic level, demographic information, and geographic location data in 184 countries and 1241 areas from December 18, 2019, to September 30, 2020, were developed from public reports released by national health authorities and bureau of statistics. Each estimator is trained on a distinct bootstrap sample of the. Contribute to AntoineAugusti/bagging-boosting-random-forests development by creating an account on GitHub. Random Forests (RF) Base estimator: Decision Tree, Logistic Regression, Neural Network. Recently, the analysis and forecast of the COVID-19 pandemic have attracted worldwide attention. Bagging, boosting and random forests in Matlab. The rapid geographic spread of COVID-19, to which various factors may have contributed, has caused a global health crisis.
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