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Journal of Data Science


BIE: Binary Image Encoding for the Classification of Tabular Data
Volume 23, Issue 1 (2025), pp. 109–129
Pub. online: 19 April 2024      Type: Data Science In Action      Open accessOpen Access
Received
13 October 2023
Accepted
13 February 2024
Published
19 April 2024

Abstract

There has been remarkable progress in the field of deep learning, particularly in areas such as image classification, object detection, speech recognition, and natural language processing. Convolutional Neural Networks (CNNs) have emerged as a dominant model of computation in this domain, delivering exceptional accuracy in image recognition tasks. Inspired by their success, researchers have explored the application of CNNs to tabular data. However, CNNs trained on structured tabular data often yield subpar results. Hence, there has been a demonstrated gap between the performance of deep learning models and shallow models on tabular data. To that end, Tabular-to-Image (T2I) algorithms have been introduced to convert tabular data into an unstructured image format. T2I algorithms enable the encoding of spatial information into the image, which CNN models can effectively utilize for classification. In this work, we propose two novel T2I algorithms, Binary Image Encoding (BIE) and correlated Binary Image Encoding (cBIE), which preserve complex relationships in the generated image by leveraging the native binary representation of the data. Additionally, cBIE captures more spatial information by reordering columns based on their correlation to a feature. To evaluate the performance of our algorithms, we conducted experiments using four benchmark datasets, employing ResNet-50 as the deep learning model. Our results show that the ResNet-50 models trained with images generated using BIE and cBIE consistently outperformed or matched models trained on images created using the previous State of the Art method, Image Generator for Tabular Data (IGTD).

Supplementary material

 Supplementary Material
We provide the code and datasets separately in the supplementary material. Included in the code is also all of the figures included in the paper in svg, pdf, and png format. The code reflects the contents of the github repository used for these experiments at the time of publication (Halladay et al., 2023).

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Copyright
2025 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.
Open access article under the CC BY license.

Keywords
computer vision DeepInsight IGTD native representation ResNet-50 tabular-to-image

Funding
This work was supported by the State of Colorado through funds appropriated for cybersecurity by a piece of legislation dubbed “Cyber Coding Cryptology for State Records.”

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