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 10 of 10 for “"tree ensemble"”.
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Randomness In Tree Ensemble Methods
Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical …
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Randomness In Tree Ensemble Methods
Tree ensembles have proven to be a popular and powerful tool for predictive modeling tasks. The theory behind several of these methods (e.g. boosting) has received considerable attention. However, other tree ensemble techniques (e.g. bagging, random forests) have attracted limited theoretical …
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Prescriptive analytics in operations problems : a tree ensemble approach
The main contributions of this thesis concern addressing challenges in the field of prescriptive optimization, and how machine learning techniques can be incorporated into solving data-driven operational optimization problems. In chapter 2, we provide a data-driven study of the secondary ticket …
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Nonparametric High-dimensional Models: Sparsity, Efficiency, Interpretability
This thesis explores ensemble methods in machine learning, a technique that builds a predictive model by jointly training simpler base models. It examines three types of ensemble methods: additive models, tree ensembles, and mixtures of experts. Each ensemble method is characterized by a specific …
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Analytics and Decision Making in Sustainable Operations
… we introduce a deterministic approach in which a tree-ensemble model, specifically a random forest, forecasts how much drivers use their EV. This gives rise to a challenge from the predict-then-optimize literature around the tractability of optimizations in which an objective function is …
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A new filtering method for improving the quality of variant discovery
… fail to run in some cases. We propose Variant Ensemble Filter (VEF), a variant filtering tool based on decision tree ensemble methods that overcomes the main drawbacks of VQSR and HF. Contrary to these methods, we treat filtering as a supervised learning problem, using variant call data with …
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Systems Pharmacology – Machine Learning Approaches in Profiling Oncology Drug Candidates
… Artificial neural nets (ANN), and Decision trees – classification and regression tree (CART) and multi-tree majority voting ensemble techniques i.e., random forest and XGBoost.The feature sets for building these models were extracted by computing chemical fingerprints and quantum chemical …
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Statistical-Based Hydrological Simulation and Inference
… include (1) a stepwise clustered regression tree ensemble (SCRTE) model that addresses the spatial autocorrelation of daily streamflow; (2) a Wilks feature importance (WFI) method that provides reliable variable rankings for supporting hydrological inference and simulation (through a stepwise …
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Predictive and Prescriptive Analytics in Operations Management
… and predictive power within the context of tree ensembles. The first chapter introduces the Extended Sampled Trees (XSTrees) method, a novel tree ensemble ML method for classification and regression. Instead of learning a single decision tree like CART, or an collection of trees like Random …
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Survival Prediction For Brain Tumor Patients Using Gene Expression Data
… predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for …