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

TL;DR

ConvGeN introduces a novel deep generative model that leverages convex space learning to generate synthetic minority class data, significantly improving classification performance on small imbalanced tabular datasets compared to existing deep models.

Contribution

The paper proposes ConvGeN, a deep generative model that combines convex space learning with deep networks, outperforming existing deep models and matching linear interpolation methods on small imbalanced datasets.

Findings

ConvGeN outperforms existing deep generative models on small imbalanced tabular datasets.

ConvGeN achieves classification performance comparable to linear interpolation approaches.

ConvGeN enhances synthetic tabular data generation beyond imbalanced classification applications.

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.