Table2Image: Interpretable Tabular Data Classification with Realistic Image Transformations
This work addresses the problem of interpretability and scalability in tabular data classification for machine learning practitioners, though it appears incremental as it builds on existing deep learning and interpretability methods.
The paper tackles the challenge of interpretable and lightweight deep learning for tabular data by introducing Table2Image, a framework that transforms tabular data into realistic images, achieving competitive classification performance with superior accuracy and area under the curve on benchmark datasets.
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.