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Open Access. Powered by Scholars. Published by Universities.®

2019

Operations Research, Systems Engineering and Industrial Engineering

Bi-level Optimization

Articles 1 - 3 of 3

Full-Text Articles in Engineering

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Mohsen Shahhosseini

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Aug 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jan 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Industrial and Manufacturing Systems Engineering Publications

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...