发表机构
Rey Juan Carlos University (URJC); Universidad Francisco de Vitoria; University of Castilla-La Mancha; Yachay Tech University（雷胡安·卡洛斯大学; 弗朗西斯科·德·维多利亚大学; 卡斯蒂利亚-拉曼恰大学; 亚奇理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现有表格转图像方法丢失特征关系的问题，提出基于自组织映射的TabSOM方法，其在二分类数据集基准测试中表现优异且可解释性良好，缩小了表格数据深度学习的性能与可解释性差距。
AI 中文摘要
表格转图像方法已成为利用卷积神经网络和视觉Transformer高预测性能的新型方法，它们将表格数据转换为图像表示，通过降维方法（如t-SNE、UMAP、PCA）将每个特征映射到固定像素位置。然而，这些方法仅编码每个特征的边际值，丢弃了特征关系信息。我们提出TabSOM，一种基于自组织映射（Self-Organizing Map，SOM）的表格转图像编码，它提供：（i）空间布局，其中每个输入特征通过无碰撞匈牙利分配从其分量平面获得固定画布位置；（ii）图，捕获从SOM分量平面导出的成对特征关系。生成的图像堆叠两个多尺度节点通道：一个编码固定尺度下的特征值，另一个编码成对特征交互作为相关特征之间的空间连接。引入两种基于SOM的可解释性方法：受原型启发的部分依赖图和类分离重要性分数。在公共二分类数据集上与12种现有表格转图像方法进行基准测试，TabSOM在每个数据集上排名第一或第二，且在所评估的所有方法中实现最低方差。针对随机森林（Random Forest）、XGBoost和SHAP验证了TabSOM获得的可解释性，类分离分数在排名靠前的特征上与已建立的基线显示出合理一致性，同时捕获输入数据的互补结构信息。这些结果表明，TabSOM提供了一种将深度学习架构应用于表格数据的有效且可解释的方法，缩小了该领域的性能-可解释性差距。
英文摘要
Tabular-to-image methods have emerged as novel approaches to leverage the high predictive performance of convolutional neural networks and vision transformers. They convert tabular data into image representations, mapping each feature at a fixed pixel location derived from a dimensionality-reduction method (e.g., t-SNE, UMAP, PCA). However, they encode only the marginal value of each feature and discard information about feature relationships. We propose TabSOM, a tabular-to-image encoding built on the Self-Organizing Map (SOM), which provides: (i) a spatial layout in which every input feature occupies a fixed canvas position derived from its component plane via collision-free Hungarian assignment; and (ii) a graph that captures pairwise feature relationships derived from the SOM component planes. The resulting image stacks two multi-scale node channels: one encodes feature values at fixed scales, while the other encodes pairwise feature interactions as spatial connections between related features. Two SOM-derived interpretability approaches are introduced: a prototype-inspired partial dependence plot and a class--separation importance score. Benchmarked against twelve existing tabular-to-image methods across public binary-classification datasets, TabSOM ranks first or second on every dataset and achieves the lowest variance of any method evaluated. Interpretability obtained with TabSOM was validated against Random Forest, XGBoost, and SHAP, the class-separation score shows reasonable agreement with established baselines on the top-ranked features while capturing complementary structural information from input data. These results demonstrate that TabSOM provides an effective and interpretable approach for applying deep learning architectures to tabular data, bridging the performance--interpretability gap in this domain.
