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 82 for “"Class imbalance"”.
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A Novel Penalized Log-likelihood Function for Class Imbalance Problem
… is based on assumptions that the target classes are equally distributed and the overall accuracy is maximized, which do not apply to class imbalance problems (e.g., fraud detection, rare disease diagnoses, customer conversion prediction, cybersecurity, predictive maintenance). When …
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Advances in NLP Algorithms on Unstructured Medical Notes Data and Approaches to Handling Class Imbalance Issues
… projects, each addressing one aspect of the text classification tasks on unstructured medical notes.</p> <p>The first study investigated the model performance of sequence deep learning models that are widely used in NLP tasks such as RNN, GRU, LSTM, Bi-LSTM, as well as CNN and the novel and more …
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Advancing Cross-Domain Fake News Detection: Enhanced Models to Improve Generalization and Tackle the Class Imbalance Problem
… Among these, cross-domain generalization and class imbalance are two critical problems that considerably impact the performance of detection systems. Although there are multiple challenges in FND, this thesis focuses on these two problems due to their widespread influence across various …
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Integrating Gradient Boosting and Generative Models: Hybrid Approach to Address Class Imbalance and Evaluation Gaps in Real-World Systems
… challenge in machine learning due to the extreme class imbalance, high cost of false negatives, and the need to regulate false positives in realworld settings at scale. This thesis introduces Tail-end FPR Max Recall, a business-aware evaluation framework designed for such constrained environments. …
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Learning from small and imbalanced dataset of images using generative adversarial neural networks.
… supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques …
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Novel machine learning approaches for modeling variations in semiconductor manufacturing
… processes are developed. Challenges include class imbalance, concept drift (temporal variation) and feature selection. Batch and online learning methods are introduced to overcome the class imbalance. Incremental learning frameworks are developed to handle concept drift and class imbalance …
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Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.
Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions …
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Methods for imbalanced data in sports analytics: improving injury prediction models
… modeling and evaluating prediction under extreme class imbalance, rather than to identify a single best-performing classifier. Experiments use XGBoost as a standard baseline and examine how methodological choices affect model behavior, including regularization with early stopping to control …
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Developing Learning Methods for Non-stationary and Imbalanced Data Streams
… streams generated by real-world applications are imbalanced within themselves. This difficulty is more acute in multi-class learning tasks. Despite "learning from non-stationary streams" and "class imbalance" problems having been investigated separately in the literature, too little attention has …
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A replication of a cost-sensitive decision tree approach for fraud detection
… with widespread implications. However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each class. Sahin et al. [7] define a …
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Improving Text Classification Using Graph-based Methods
Text classification is a fundamental natural language processing task. However, in real-world applications, class distributions are usually skewed, e.g., due to inherent class imbalance. In addition, the task difficulty changes based on the underlying language. When rich morphological structure and …
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Decision tree rule-based feature selection for imbalanced data
A class imbalance problem appears in many real world applications, e.g., fault diagnosis, text categorization and fraud detection. When dealing with an imbalanced dataset, feature selection becomes an important issue. To address it, this work proposes a feature selection method that is based on a …
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"Towards Closed-Loop Sleep Monitoring in Parkinson’s Disease: Self-Supervised Learning Strategies for Sleep Stage Classification"
… but requires accurate, real-time sleep-stage classification from subthalamic nucleus signals, a task where conventional models generalize poorly. To address pronounced class imbalance and improve cross-patient generalization, this work introduces a self-supervised transformer framework. The …
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