Global ETD Search
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Showing 1 to 5 of 5 for “"Class imbalanced data"”.
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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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Multiple classifier combination through ensembles and data generation
This thesis introduces new approaches, namely the DataBoost and DataBoost-IM algorithms, to extend Boosting algorithms' predictive performance. The DataBoost algorithm is designed to assist Boosting algorithms to avoid over-emphasizing hard examples. In the DataBoost algorithm, new synthetic data …
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Automatic Concrete Defect Identification by Silencing Features of Deep Neural Network
… gradient vanishing problem is very prominent on class imbalanced data-setssuch as crack detection. In this work, a deep neural architecture is proposed foralleviating the effect gradient vanishing problem. Furthermore, A feature silencingmodule is incorporated in the crack detection framework, …
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Progress on the Interplay of Machine Learning and Optimization
… of machine learning models, and improving data for prediction. In Chapter 2 and 3, we focus on improving the interpretability of machine learning models. In particular, Chapter 2 presents an efficient algorithm for training high-quality Nonlinear Oblique Classification Trees using gradient …
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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 …