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.
Results
Showing 1 to 20 of 25 for “"data imbalance"”.
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A Study on Data Imbalance: Using Metrics on Input Data to Foresee Bias and Fairness in Classification Outcomes
Abstract presente tra gli allegati
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Fitting AdaBoost Models From Imbalanced Data with Applications in College Basketball
Data imbalance is an important consideration when working with real world data. Over/undersampling approaches allow us to gather more insight from the limited data we have on the minority class; however, there are many proposed methods. The goal of our study is to identify the optimal approach for …
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Improving network pavement performance management using machine learning
… resides in the discrepancy between the data used for model development and the network-level pavement performance data. This dissertation tackles this issue by developing pavement performance models with variables that can be readily accessed from network pavement management systems …
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Multi-class segmentation of brain tumor using Convolution Neural Network
… The two phase training and entropy sampling of data makes it easier to learn tumor boundaries and overcome the data imbalance problem.
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Generative AI for osteoarthritis imaging: CycleGAN-based synthesis and U-net3D segmentation
… for medical imaging, particularly for lowering data imbalance and increasing diagnostic accuracy. This thesis investigates the application of CycleGAN and Unet3D in two medical imaging tasks: hand osteoarthritis (HOA) and knee effusion. The experiment results demonstrate promising findings in …
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Machine Learning Approaches that Extend Healthcare: Algorithms & Applications
… addressing core challenges in real-world medical data which encompass four main axes: • Label Scarcity: The thesis presents a novel self-supervised learning scheme that learns periodic and frequency information in data without labels, enabling representation learning for periodic tasks like vital …
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Extensive Huffman-tree-based neural network for the imbalanced dataset and its application in accent recognition
To classify the data-set featured with a large number of heavily imbalanced classes, this thesis proposed an Extensive Huffman-Tree Neural Network (EHTNN), which fabricates multiple component neural network-enabled classifiers (e.g., CNN or SVM) using an extensive Huffman tree. Any given node in …
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A New Generative Adversarial Network for Improving Classification Performance for Imbalanced Data
Data is a common issue in many industries, particularly in fields such as fraud detection and medical diagnosis. Imbalanced data refers to datasets where the distribution of classes is not equal, resulting in an over- representation of one class and an under-representation of another. This can lead …
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Data-driven Algorithms for Critical Detection Problems: From Healthcare to Cybersecurity Defenses
Machine learning and data-driven approaches have been widely applied to critical detection problems, but their performance is often hindered by data-related challenges. This dissertation seeks to address three key challenges: data imbalance, scarcity of high-quality labels, and excessive data …
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Reliable and decentralised deep learning for physiological data
Physiological data encompass measurements from various bodily functions and processes. By employing machine learning to model these data, especially with the advancement of mobile sensing technologies, it becomes feasible to automatically and continually monitor and diagnose one's health status. …
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Learning Representations for Limited and Heterogeneous Medical Data
Data insufficiency and heterogeneity are challenges of representation learning for machine learning in medicine due to the diversity of medical data and the expense of data collection and annotation. To learn generalizable representations from such limited and heterogeneous medical data, we aim to …
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Deep Learning Approach for Cell Nuclear Pore Detection and Quantification over High Resolution 3D Data
… nuclear pores in high-resolution 3D microscopy data is critical for cellular biology and disease research. This thesis introduces a deep learning pipeline crafted to automate the segmentation and quantification of nuclear pores from high-resolution 3D cell organelle images. Our aim is to refine …
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Prediction of Large for Gestational Age Infants in Ethnically Diverse Datasets Using Machine Learning Techniques. Development of 3rd Trimester Machine Learning Prediction Models and Identification of Important Features Using Dimensionality Reduction Techniques
… LGA prediction models for ethnically diverse datasets and provide a benchmark for future LGA prediction work. Methods: Two retrospective datasets were used: Born In Bradford (BiB) and NHS, each including a large percentage of women of South Asian ethnicity. After appropriate data preparation, …
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Leveraging Transformer Models and Elasticsearch to Help Prevent and Manage Diabetes through EFT Cues
… from these EFT cues. However, class imbalance often presents a challenging issue when dealing with such domain-specific data. To mitigate this issue, this research employs Elasticsearch to address data imbalance and enhance the machine learning (ML) pipeline for improved accuracy of …
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Predicting mergers and acquisitions using machine learning
… were trained on a comprehensive historical dataset with diverse financial indicators. Given the considerable amount of missing values in the dataset, imputation was applied to allow all algorithms to function properly. Feature selection was conducted to remove redundant features, mitigating …
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Toward a General Novelty Detection Framework in Structural Health Monitoring; Challenges and Opportunities in Deep Learning
… (SHM) is an anomaly detection process. Data-driven SHM has gained much attention compared to the model-based strategy, specifically with the current state-of-the-art machine learning routines. Model-based methods require structural information, time-consuming model updating, and may fail …
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Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk
… RP factors is difficult because existing data are under-sampled and imbalanced. Support vector machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem …
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Assessing Climatic Hazards in Coastal Socio-Ecological Systems using Complex System Approaches
… developed in this work are able to address data imbalance and improve models' capacity to classify and estimate damage occurrence, which depends on multiple geographical, seasonal, and climatic factors. Collectively, this work demonstrates the potential for advanced modeling techniques to …
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Develop innovative methodology to optimally fill in missing values and predict progression on multiple sclerosis
… MS progression, is effectively managing missing data in MS datasets. This study introduces an innovative sequential Multi-Imputation (MI) bootstrapping method to address the challenge of missing data in MS datasets. Initially, several ML algorithms, including k-Nearest Neighbors (kNN), Random …
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