Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 6 of 6 for “"Boosting Methods"”.
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Hospital readmission risk
… tools and advanced statistical learning methods for predicting hospital readmission risk. The meth ods considered include the LACE score, decision trees, logistic regression, random forests, gradient-boosting methods, and neural networks. The study uses data from South Africa's privately …
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Interpretable machine learning methods with applications to health care
… in recent years, black-box algorithms like boosting methods or neural networks play more important roles in the real world. However, interpretability is a severe need for several areas of applications, like health care or business. Doctors or managers often need to understand how models make …
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Machine Learning Methods for Wastewater Treatment Plants
… tree-based algorithms, particularly gradient boosting methods such as LightGBM. This model was implemented in real plants as a Decision Support System that can alert plant operators, and subsequently integrated into a new aeration controller that automatically reacts to events without the need …
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Spatial Infectious Disease Transmission Models: Variable Screening Methods and Logistic Formulation.
… and compare various variable screening methods for individual-level disease transmission models. The methods include least absolute shrinkage and selection operator (Lasso), forward and backward stepwise Akaike information criterion (AIC), variable random selection (boosting) methods, …
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Fitting AdaBoost Models From Imbalanced Data with Applications in College Basketball
… minority class; however, there are many proposed methods. The goal of our study is to identify the optimal approach for over/undersampling to use with Adaptive Boosting (AdaBoost). Based on a simulation study, we’ve found that combining AdaBoost with various sampling techniques provides an …
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Predictive and Prescriptive Analytics in Operations Management
… novel Machine Learning (ML) and optimization methods in (i) predictive analytics, (ii) prescriptive analytics, and (iii) their high-impact applications in operations management. On the predictive side, this thesis tackles the problems of interpretability and predictive power within the context …