A Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 99129 - 99149

Published: Jan. 1, 2022

Ensemble learning techniques have achieved state-of-the-art performance in diverse machine applications by combining the predictions from two or more base models. This paper presents a concise overview of ensemble learning, covering three main methods: bagging, boosting, and stacking, their early development to recent algorithms. The study focuses on widely used algorithms, including random forest, adaptive boosting (AdaBoost), gradient extreme (XGBoost), light (LightGBM), categorical (CatBoost). An attempt is made concisely cover mathematical algorithmic representations, which lacking existing literature would be beneficial researchers practitioners.

Language: Английский

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Short-term forecasting COVID-19 cumulative confirmed cases: Perspectives for Brazil

Chaos Solitons & Fractals, Journal Year: 2020, Volume and Issue: 135, P. 109853 - 109853

Published: May 1, 2020

Language: Английский

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Compressive strength prediction of fly ash-based geopolymer concrete via advanced machine learning techniques

Case Studies in Construction Materials, Journal Year: 2021, Volume and Issue: 16, P. e00840 - e00840

Published: Dec. 9, 2021

Concrete is a widely used construction material, and cement its main constituent. Production utilization of severely affect the environment due to emission various gases. The application geopolymer concrete plays vital role in reducing this flaw. This study supervised machine learning algorithms, decision tree (DT), bagging regressor (BR), AdaBoost (AR) estimate compressive strength fly ash-based concrete. coefficient determination (R2), mean absolute error, square root error were evaluate model's performance. performance was further confirmed using k-fold cross-validation technique. Compared DT AR model, model more effective predicting results, with an R2 value 0.97. lesser values errors (MAE, MSE, RMSE) higher clear indications better model. Additionally, sensitivity analysis conducted ascertain degree contribution each parameter towards prediction results. techniques predict concrete's mechanical properties will benefit area civil engineering by saving time, effort, resources.

Language: Английский

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Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Materials, Journal Year: 2021, Volume and Issue: 14(4), P. 794 - 794

Published: Feb. 8, 2021

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on comparison between individuals and ensemble approaches, such as bagging. Optimization bagging done by making 20 sub-models to depict accurate one. Variables like cement content, fine coarse aggregate, water, binder-to-water ratio, fly-ash, superplasticizer modeling. Model performance evaluated various statistical indicators mean absolute error (MAE), square (MSE), root (RMSE). Individual show a moderate bias result. However, model gives better result with R2 = 0.911 compared decision tree (DT) gene expression programming (GEP). K-fold cross-validation confirms model’s accuracy R2, MAE, MSE, RMSE. Statistical checks reveal that provides 25%, 121%, 49% enhancement errors RMSE target outcome response.

Language: Английский

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A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications

Applied Sciences, Journal Year: 2022, Volume and Issue: 12(17), P. 8654 - 8654

Published: Aug. 29, 2022

Machine learning algorithms are increasingly used in various remote sensing applications due to their ability identify nonlinear correlations. Ensemble have been included many practical improve prediction accuracy. We provide an overview of three widely ensemble techniques: bagging, boosting, and stacking. first the underlying principles present analysis current literature. summarize some typical algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, spatial downscaling climate parameters land surface temperature. Finally, we suggest future directions for using applications.

Language: Английский

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Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

Remote Sensing, Journal Year: 2020, Volume and Issue: 12(7), P. 1095 - 1095

Published: March 29, 2020

Understanding the spatial distribution of soil organic carbon (SOC) content over different climatic regions will enhance our knowledge gains and losses due to change. However, little is known about SOC in contrasting arid sub-humid Iran, whose complex SOC–landscape relationships pose a challenge analysis. Machine learning (ML) models with digital mapping framework can solve such relationships. Current research focusses on ensemble ML increase accuracy prediction. The usual method boosting or weighted averaging. This study proposes novel technique: stacking multiple through meta-learning model. In addition, we tested rescanning covariate space maximize prediction accuracy. We first applied six state-of-the-art (i.e., Cubist, random forests (RF), extreme gradient (XGBoost), classical artificial neural network (ANN), based model averaging (AvNNet), deep networks (DNN)) predict map at depth intervals for both regions. with/without were Out models, DNN resulted best modeling accuracies, followed by RF, XGBoost, AvNNet, ANN, Cubist. Importantly, indicated significant improvement content, especially when combined space. For instance, RMSE values upper 0–5 cm profiles site proposed approaches 17% 9% respectively, less than that obtained models—the individual indicates original extract more information improve Overall, results suggest diverse sets could be used accurately estimate

Language: Английский

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Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM

Engineering Applications of Artificial Intelligence, Journal Year: 2020, Volume and Issue: 91, P. 103573 - 103573

Published: Feb. 27, 2020

Language: Английский

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A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting

Energy, Journal Year: 2020, Volume and Issue: 216, P. 119174 - 119174

Published: Nov. 2, 2020

Language: Английский

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Forecasting wholesale prices of yellow corn through the Gaussian process regression

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(15), P. 8693 - 8710

Published: March 1, 2024

Language: Английский

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Forecast combinations: An over 50-year review

International Journal of Forecasting, Journal Year: 2022, Volume and Issue: 39(4), P. 1518 - 1547

Published: Dec. 20, 2022

Language: Английский

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