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 9 of 9 for “"Imbalanced learning"”.
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Novel Instance-Level Weighted Loss Function for Imbalanced Learning
Binary classification using imbalanced datasets remains a challenge. Typically, supervised learning algorithms minimize the binary cross-entropy objective function to determine the final parameter estimates. This objective function assumes an equal class distribution between the minority (i.e. …
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Imbalanced learning using actuarial modified loss function in tree-based models
The point mass at zero and the heavy tail of insurance loss distribution poses the challenge to apply traditional methods directly to claim loss modeling. Via an illustrative simple dataset, this thesis first pinpoints the pitfall in the traditional tree-based algorithm’s splitting function. This …
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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
… inaccurate. Objective: application of machine learning methods to develop 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 …
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Supervised Classification of Imbalanced Bidding Fraud Data
… and SMOTE-TomekLink) methods to solve the imbalanced learning problem. We utilize the Randomized Search Cross Validation to tune the hyper-parameters for Support Vector Machine (SVM), Random Forest and default parameters for Artificial Neural Network (ANN) with MLP classifier, finally, to …
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Toward Improved Classification of Imbalanced Data
… years. However, many real-world datasets are imbalanced. Learning from imbalanced data poses major challenges and is recognized as needing significant research. The problem with imbalanced data is the performance of learning algorithms in the presence of underrepresented data and severely …
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Learning With An Insufficient Supply Of Data Via Knowledge Transfer And Sharing
<p>As machine learning methods extend to more complex and diverse set of problems, situations arise where the complexity and availability of data presents a situation where the information source is not "adequate" to generate a representative hypothesis. Learning from multiple sources of data is a …
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Improving Clinical Prediction Models with Statistical Representation Learning
… dissertation studies novel statistical machine learning approaches for healthcare risk prediction applications in the presence of challenging scenarios, such as rare events, noisy observations, data imbalance, missingness and censoring. Such scenarios manifest frequently in practice, and they …
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Machine learning approaches to improving mispronunciation detection on an imbalanced corpus
… performance of which is heavily affected by the imbalanced distribution of the classes in a manually annotated data set of non-native English (Read Aloud responses from the TOEFL Junior Pilot assessment). In order to address problems caused by this extreme class imbalance, two machine learning …
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Data-Driven Approaches in Water Pipe Condition Assessment and Failure Prediction
… of buried water pipes, combining machine learning techniques, numerical methods, and physical modelling to improve failure prediction and support proactive asset management. A key contribution of this work is the development of methods to address the common issue of class imbalance in pipe …