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 20 of 75 for “"Ensemble learning"”.
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Ensemble Learning Methods for Educational Data Mining Applications
… aimed at assessing instructional practices and learning environments by evaluating the success of and characterizing student subgroups that may benefit from such modalities. We develop an ensemble learning approach to perform these analytics tasks with specific focus on estimating individualized …
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Ensemble Learning Techniques for Structured and Unstructured Data
… an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model …
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Advancing Community Detection through Ensemble Learning and Modularity Maximization
… NP-complete problem. Recently, a machine-learning algorithmic scheme was introduced that uses information within a set of partitions to find a new partition that better maximizes an objective function. The scheme, known as RenEEL, uses extremal ensemble learning. Starting with an ensemble …
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Online ensemble learning in the presence of concept drift
In online learning, each training example is processed separately and then discarded. Environments that require online learning are often non-stationary and their underlying distributions may change over time (concept drift). Even though ensembles of learning machines have been used for handling …
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Cluster-enhanced Ensemble Learning for Mapping Surface Ozone in China
… This research employs cluster-enhanced ensemble learning methodologies. First, the K-means clustering algorithm categorizes ozone-related data, providing a basis for further analysis. The optimal cluster number is determined using the elbow method. Next, various ensemble learning models, …
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Penalised regression for high-dimensional data: an empirical investigation and improvements via ensemble learning
… settings and propose new methodology that uses ensemble learning to enhance the performance of these methods. The relative efficacy of different penalised regression methods in finite-sample settings remains incompletely understood. Through a large-scale simulation study, consisting of more than …
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Utilizing data mining techniques and ensemble learning to predict development of surgical site infections in gynecologic cancer patients
<p>Surgical site infections are costly to both patients and hospitals, increase patient mortality, and are the most common form of a hospital acquired infection. Gynecological cancer surgery patients are already at higher risk of developing an infection due to the suppression of their immune …
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Fair, Robust, and Calibrated Deep Learning with Heavy-Tailed Subgroups
To deploy safe machine learning systems in the real world, we must ensure they are fair, robust, and calibrated. However, heavy-tails pose a challenge to this mandate, especially since real world data is often imbalanced and marginalized subgroups tend to be underrepresented. To move toward safer …
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Explainable AI Methods For Enhancing AI-Based Network Intrusion Detection Systems
… the thesis addresses the potential of ensemble learning techniques in improving AI-based network intrusion detection by proposing a two-level ensemble learning framework comprising base learners and ensemble methods trained on input datasets to generate evalua tion metrics and new …
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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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Enhancing Risk Stratification for Substance Use Disorder, Depression, and Anxiety through Quantitative Predictive Analytics
… plan data. The research utilizes several machine learning algorithms including Logistic Regression, Random Forest, Support Vector Machines (SVM), XGBoost, K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Neural Networks, CatBoost, and Ensemble Learning. By examining medical diagnoses from …
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Automated test case prioritization using machine learning for large scale continuous integration environments
… the prioritization process by leveraging machine learning (ML) models. However, an automated and scalable solution is required to address the complexity of feature extraction and selection, ML model tuning, and evaluation in large-scale CI environments. This thesis aims to integrate ML techniques …
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Novel Machine Learning Models for Identification, Characterization and Prioritization of Phenotype-Genotype Associations
… the applicability of sparse supervised learning models for association studies of complex traits. In particular, I investigate integrative machine learning models that can leverage additional biological knowledge about phenotypes and genetic variants while searching for genetically …
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Adaptive systems for DDoS attacks detection and mitigation in IoT networks
… the third objective is the design of a Deep Ensemble Learning with Pruning (DEEPShield) system that integrates CNN and LSTM architectures, optimized through post-training pruning and a novel preprocessing method. This system achieves high detection accuracy with low resource demand, suitable …
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Eye tracking for the iPhone using deep learning
… from the previous benchmark) was achieved using ensemble learning with the ResNet10 model with batch normalization.
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A Multiple Classifier System for Predicting Best-Selling Amazon Products
… this research is the first application of ensemble learning to Amazon product data of this type and the first use of product images and Convolutional Neural Networks to predict product success.</p>
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A Machine Learning Model for Octane Number Prediction
… in the form of phenomeno-logical and machine learning models (Gonz´alez 2019). Phenomeno-logical models have been used in the past as a way of programming an engineer's thought process in the form of differential equations put together. Machine learning models are data driven with primarily …
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