将表格数据转为图像，用深度学习解决小样本分类难题
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations
- 把异构表格数据转为图像表示，适配深度模型
- 在小样本场景下性能超越传统方法和专用深度模型
- 适合医疗等数据稀缺领域的表格分类任务
本研究针对医疗等领域的表格数据分类中普遍存在的数据稀缺问题，提出Tab2Visual方法。该方法将异构表格数据转化为视觉表示，使强大的深度学习模型得以应用。通过引入新颖的图像增强技术和迁移学习机制，有效缓解数据不足问题。在多种表格数据集上进行了广泛评估，对比了经典机器学习、树集成方法以及专为表格设计的先进深度学习模型。深入分析了影响性能的关键因素。实验结果表明，在小样本条件下，Tab2Visual显著优于其他方法。
原文摘要 · Abstract (English)
This research addresses the challenge of limited data in tabular data classification, particularly prevalent in domains with constraints like healthcare. We propose Tab2Visual, a novel approach that transforms heterogeneous tabular data into visual representations, enabling the application of powerful deep learning models. Tab2Visual effectively addresses data scarcity by incorporating novel image augmentation techniques and facilitating transfer learning. We extensively evaluate the proposed approach on diverse tabular datasets, comparing its performance against a wide range of machine learning algorithms, including classical methods, tree-based ensembles, and state-of-the-art deep learning models specifically designed for tabular data. We also perform an in-depth analysis of factors influencing Tab2Visual's performance. Our experimental results demonstrate that Tab2Visual outperforms other methods in classification problems with limited tabular data.
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