BIE: Binary Image Encoding for the Classification of Tabular Data
Bibliographic record
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".