VG-TIE：一种基于可见性图的可解释表格到图像编码方法

中文摘要 AI 辅助

表格到图像编码方法使得基于卷积神经网络和视觉变换器的模型能够应用于表格数据，将特征向量转换为图像。现有方法采用线性和非线性降维技术（如主成分分析（PCA）、t-SNE和UMAP）来确定像素位置，导致生成的图像空间布局不能固有地反映特征关系。本文介绍了用于表格到图像编码的可见性图（VG-TIE），这是一种新颖的方法，利用自然可见性图（NVG）和水平可见性图（HVG）将特征值的结构编码到通过PCA获得的二维空间中。生成的图像是模型无关的且本质上可解释的。每个像素对应一个输入特征，其强度反映了与总体均值的偏差幅度和方向，边缘表示特征之间正式定义的可见性关系。VG-TIE提供了两种可解释性方法：（i）基于节点度分布的特征排序；（ii）基于像素强度结合Grad-CAM的局部和全局特征重要性。在六个公开表格数据集上的实验表明，VG-TIE与其他表格到图像方法相比具有竞争力，同时提供了与内在可解释方法相似的特征重要性和排序的可解释性。结果凸显了所提出的基于图像的转换在扩展深度学习在表格数据领域的应用方面的潜力。

英文摘要

Tabular-to-image encoding methods enable the application of models based on both convolutional neural networks and vision transformers to tabular data, transforming feature vectors into images. Existing methods employ linear and nonlinear dimensionality reduction techniques (e.g., Principal Component Analysis (PCA), t-SNE, and UMAP) to determine pixel positions, resulting in images whose spatial layout do not inherently reflect feature relationships. This paper introduces Visibility Graphs for Tabular-to-Image Encoding (VG-TIE), a novel method that encodes the structure of feature values using Natural Visibility Graph (NVG) and Horizontal Visibility Graph (HVG) into a two-dimensional space obtained through PCA. The resulting images are model-agnostic and intrinsically interpretable. Each pixel corresponds to an input feature, its intensity reflects the magnitude and direction of deviation from the population mean, and edges represent formally defined visibility relationships between features. VG-TIE provides two interpretability methods: (i) feature ranking from node degree distributions; and (ii) local and global feature importance from pixel intensity combined with Grad-CAM. Experiments on six public tabular datasets show that VG-TIE is competitive with other tabular-to-image methods while providing interpretability on feature importance and ranking similar to intrinsic interpretable methods. The results highlight the potential of the proposed image-based transformation to provide an effective framework that expands the use of deep learning across tabular data domains.

发表机构

• Rey Juan Carlos University（胡安·卡洛斯国王大学）
• University of Castilla-La Mancha（卡斯蒂利亚-拉曼恰大学）
• Yachay Tech University（亚查伊理工大学）

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