Table2Image: Interpretable Tabular Data Classification with Realistic Image Transformations

TL;DR

Table2Image transforms tabular data into realistic images to leverage deep learning for classification, combining statistical feature analysis with interpretability tools to improve accuracy and understanding.

Contribution

The paper introduces a novel image-based framework for tabular data classification, incorporating VIF initialization and interpretability methods like SHAP for enhanced performance and transparency.

Findings

Achieves superior accuracy and AUC on benchmark datasets.

Provides interpretable insights combining tabular and image representations.

Demonstrates scalability and robustness of the lightweight approach.

Abstract

Recent advancements in deep learning for tabular data have shown promise, but challenges remain in achieving interpretable and lightweight models. This paper introduces Table2Image, a novel framework that transforms tabular data into realistic and diverse image representations, enabling deep learning methods to achieve competitive classification performance. To address multicollinearity in tabular data, we propose a variance inflation factor (VIF) initialization, which enhances model stability and robustness by incorporating statistical feature relationships. Additionally, we present an interpretability framework that integrates insights from both the original tabular data and its transformed image representations, by leveraging Shapley additive explanations (SHAP) and methods to minimize distributional discrepancies. Experiments on benchmark datasets demonstrate the efficacy of our approach, achieving superior accuracy, area under the curve, and interpretability compared to recent leading deep learning models. Our lightweight method provides a scalable and reliable solution for tabular data classification.