Tab2Visual：利用视觉表示的深度学习克服表格数据分类中的有限数据挑战
Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations
AI总结：
针对表格数据分类中的有限数据挑战，提出Tab2Visual方法，将表格数据转换为视觉表示，结合图像增强与迁移学习，在多种数据集上评估并优于经典方法、树基集成及表格专用深度学习模型。
摘要：
本研究针对表格数据分类中的有限数据挑战，这一问题在医疗等受约束的领域尤为普遍。我们提出Tab2Visual，一种将异构表格数据转换为视觉表示的新方法，可应用强大的深度学习模型。Tab2Visual通过结合新型图像增强技术和促进迁移学习，有效解决数据稀缺问题。我们在多种表格数据集上广泛评估该方法，将其性能与多种机器学习算法对比，包括经典方法、树基集成以及专为表格数据设计的最先进深度学习模型，并深入分析影响Tab2Visual性能的因素。实验结果表明，在有限表格数据的分类问题中，Tab2Visual优于其他方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
