Summary of Table2image: Interpretable Tabular Data Classification with Realistic Image Transformations, by Seungeun Lee et al.
Table2Image: Interpretable Tabular Data Classification with Realistic Image Transformations
by Seungeun Lee, Il-Youp Kwak, Kihwan Lee, Subin Bae, Sangjun Lee, Seulbin Lee, Seungsang Oh
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Table2Image, a novel framework that transforms tabular data into realistic and diverse image representations, enabling competitive classification performance with deep learning methods. The framework addresses multicollinearity in tabular data using variance inflation factor (VIF) initialization, which enhances model stability and robustness by incorporating statistical feature relationships. An interpretability framework is also presented, integrating insights from both the original tabular data and its transformed image representations using Shapley additive explanations (SHAP) and methods to minimize distributional discrepancies. The approach demonstrates superiority in accuracy, area under the curve, and interpretability compared to recent leading deep learning models on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to turn table-based data into images that can be used with deep learning models. It solves a problem called multicollinearity by using something called variance inflation factor (VIF) initialization. This helps the model work better and make more reliable predictions. The paper also shows how to understand what’s going on inside the model, which is important for making sure it’s working correctly. The results are really good, with the new method being able to do tasks better than other deep learning models. |
Keywords
» Artificial intelligence » Classification » Deep learning
