TITLE: ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets 

ABSTRACT: Data is commonly stored in tabular format. Several fields of research are
prone to small imbalanced tabular data. Supervised Machine Learning on such
data is often difficult due to class imbalance. Synthetic data generation,
i.e., oversampling, is a common remedy used to improve classifier performance.
State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can
be used to generate synthetic samples from the convex space of the minority
class to improve classifier performance in such cases. Deep generative networks
are common deep learning approaches for synthetic sample generation, widely
used for synthetic image 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 perform
poorly compared to linear interpolation based approaches for imbalanced
classification problems on smaller tabular datasets. To overcome this, we
propose a deep generative model, ConvGeN that combines the idea of convex space
learning with deep generative models. ConvGeN learns the coefficients for the
convex combinations of the minority class samples, such that the synthetic data
is distinct enough from the majority class. Our benchmarking experiments
demonstrate that our proposed model ConvGeN improves imbalanced classification
on such small datasets, as compared to existing deep generative models, while
being at-par with the existing linear interpolation approaches. Moreover, we
discuss how our model can be used for synthetic tabular data generation in
general, even outside the scope of data imbalance and thus, improves the
overall applicability of convex space learning.
Data is commonly stored in tabular format. Several fields of research are
prone to small imbalanced tabular data. Supervised Machine Learning on such
data is often difficult due to class imbalance. Synthetic data generation,
i.e., oversampling, is a common remedy used to improve classifier performance.
State-of-the-art linear interpolation approaches, such as LoRAS and ProWRAS can
be used to generate synthetic samples from the convex space of the minority
class to improve classifier performance in such cases. Deep generative networks
are common deep learning approaches for synthetic sample generation, widely
used for synthetic image 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 perform
poorly compared to linear interpolation based approaches for imbalanced
classification problems on smaller tabular datasets. To overcome this, we
propose a deep generative model, ConvGeN that combines the idea of convex space
learning with deep generative models. ConvGeN learns the coefficients for the
convex combinations of the minority class samples, such that the synthetic data
is distinct enough from the majority class. Our benchmarking experiments
demonstrate that our proposed model ConvGeN improves imbalanced classification
on such small datasets, as compared to existing deep generative models, while
being at-par with the existing linear interpolation approaches. Moreover, we
discuss how our model can be used for synthetic tabular data generation in
general, even outside the scope of data imbalance and thus, improves the
overall applicability of convex space learning.
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets
http://arxiv.org/abs/2206.09812v2