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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

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 For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jul 2019

Optimizing Ensemble Weights For Machine Learning Models: A Case Study For Housing Price Prediction, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Guiping Hu

Designing ensemble learners has been recognized as one of the significant trends in the field of data knowledge especially in data science competitions. Building models that are able to outperform all individual models in terms of bias, which is the error due to the difference in the average model predictions and actual values, and variance, which is the variability of model predictions, has been the main goal of the studies in this area. An optimization model has been proposed in this paper to design ensembles that try to minimize bias and variance of predictions. Focusing on service sciences, two well-known ...