Title: SOM2IMG: Self-Organizing Map Based Tabular-to-Image Transformation for Deep Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: SOM, Self-organizing maps, Tabular-To-Image, T2I, Feature representation learning, Spatial feature organization, Structured data classification

Abstract: Tabular-to-image (T2I) transformation methods convert structured data into image representations to leverage the power of convolutional architectures on tabular datasets. We present SOM2IMG, a novel Self-Organizing Map (SOM) based approach that transforms tabular data into spatial image representations, facilitating the effective application of Convolutional Neural Networks (CNNs) to tabular datasets. SOM2IMG flips rows and columns so that each feature becomes a point to be organized. The SOM then creates a 2D map that reveals similar features, which serves as a blueprint for arranging features spatially in the generated image. Here, we evaluated SOM2IMG on nine diverse datasets under two comparative setups. First, against seven existing T2I methods paired with a CNN, our SOM2IMG + CNN consistently achieved the top performance across all datasets. Second, compared with other machine learning algorithms (non-neural network) applied directly to tabular data, SOM2IMG + CNN ranked first on 5 datasets and placed 2nd to 3rd on the others, primarily behind boosting algorithms. Statistical analysis against a CNN without T2I (baseline CNN) confirms SOM2IMG's substantial improvement with a large effect size, making SOM2IMG the only method to consistently outperform this baseline CNN. Although boosting methods outperformed SOM2IMG + CNN on some datasets, our results establish a strong benchmark for practitioners seeking to apply CNNs to tabular data. Unlike other transformation approaches, SOM2IMG produces interpretable spatial arrangements where related features cluster together, enabling CNN kernels to capture local feature relationships more effectively while preserving original data values, yielding both improved predictive performance and enhanced interpretability. To our knowledge, SOM2IMG is the first SOM-based T2I transformation method, demonstrating that principled spatial organization offers genuine advantages for applying deep learning to structured data.

TL;DR: Novel SOM-based method transforms tabular data to images enabling CNN use, achieves perfect 9/9 wins over existing approaches with Cohen's d=+1.08, never ranks below 3rd place overall across diverse datasets.