ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets
Oversampling is commonly used to improve classifier performance for small tabular
imbalanced datasets. State-of-the-art linear interpolation approaches can be used to
generate synthetic samples from the convex space of the minority class. Generative
networks are common deep learning approaches for synthetic sample generation. However,
their scope on synthetic tabular data generation in the context of imbalanced classification is
not adequately explored. In this article, we show that existing deep generative models …
imbalanced datasets. State-of-the-art linear interpolation approaches can be used to
generate synthetic samples from the convex space of the minority class. Generative
networks are common deep learning approaches for synthetic sample generation. However,
their scope on synthetic tabular data generation in the context of imbalanced classification is
not adequately explored. In this article, we show that existing deep generative models …
Convgen: A Convex Space Learning Approach for Deep-Generative Oversampling and Imbalanced Classification of Small Tabular Datasets
Oversampling is commonly used to improve classifier performance for small tabular
imbalanced datasets. State-of-the-art linear interpolation approaches can be used to
generate synthetic samples from the convex space of the minority class. Generative
networks are common deep learning approaches for synthetic sample generation. However,
their scope on synthetic tabular data generation in the context of imbalanced classification is
not adequately explored. In this article, we show that existing deep generative models …
imbalanced datasets. State-of-the-art linear interpolation approaches can be used to
generate synthetic samples from the convex space of the minority class. Generative
networks are common deep learning approaches for synthetic sample generation. However,
their scope on synthetic tabular data generation in the context of imbalanced classification is
not adequately explored. In this article, we show that existing deep generative models …
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