Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations
It addresses data scarcity in domains like healthcare, but appears incremental as it builds on existing visual representation and transfer learning ideas.
This research tackled the problem of limited data in tabular data classification by proposing Tab2Visual, which transforms tabular data into visual representations to apply deep learning, and it outperformed other methods in classification tasks with limited data.
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.