Contrastive learning is a model pre-training technique by first creating similar views of the original data, and then encouraging the data and its corresponding views to be close in the embedding space. Contrastive learning has witnessed success in image and natural language data, thanks to the domain-specific augmentation techniques that are both intuitive and effective. Nonetheless, in tabular domain, the predominant augmentation technique for creating views is through corrupting tabular entries via swapping values, which is not as sound or effective. We propose a simple yet powerful improvement to this augmentation technique: corrupting tabular data conditioned on class identity. Specifically, when corrupting a specific tabular entry from an anchor row, instead of randomly sampling a value in the same feature column from the entire table uniformly, we only sample from rows that are identified to be within the same class as the anchor row. We assume the semi-supervised learning setting, and adopt the pseudo labeling technique for obtaining class identities over all table rows. We also explore the novel idea of selecting features to be corrupted based on feature correlation structures. Extensive experiments show that the proposed approach consistently outperforms the conventional corruption method for tabular data classification tasks. Our code is available at https://github.com/willtop/Tabular-Class-Conditioned-SSL.

翻译：对比学习是一种模型预训练技术，其首先通过创建原始数据的相似视图，再促使数据及其对应视图在嵌入空间中接近。得益于兼具直观性与有效性的领域特定增强技术，对比学习已在图像和自然语言数据中取得成功。然而在表格数据领域，当前主流视图增强方法是通过值交换来破坏表格条目，这种方法既不够合理也不够高效。我们提出了一种对该增强技术的简单而有效的改进方案：基于类别条件破坏表格数据。具体而言，当破坏锚点行的特定表格条目时，我们不再从整张表中对该特征列的值进行均匀随机采样，而是仅从被识别为与锚点行同类的行中进行采样。我们采用半监督学习设定，并引入伪标签技术为所有表格行获取类别身份。此外，我们还探索了基于特征相关结构选择待破坏特征的新思路。大量实验表明，所提方法在表格数据分类任务中始终优于传统破坏方法。我们的代码开源在 https://github.com/willtop/Tabular-Class-Conditioned-SSL。