Recent development of deep neural networks (DNNs) for tabular learning has largely benefited from the capability of DNNs for automatic feature interaction. However, the heterogeneity nature of tabular features makes such features relatively independent, and developing effective methods to promote tabular feature interaction still remains an open problem. In this paper, we propose a novel Graph Estimator, which automatically estimates the relations among tabular features and builds graphs by assigning edges between related features. Such relation graphs organize independent tabular features into a kind of graph data such that interaction of nodes (tabular features) can be conducted in an orderly fashion. Based on our proposed Graph Estimator, we present a bespoke Transformer network tailored for tabular learning, called T2G-Former, which processes tabular data by performing tabular feature interaction guided by the relation graphs. A specific Cross-level Readout collects salient features predicted by the layers in T2G-Former across different levels, and attains global semantics for final prediction. Comprehensive experiments show that our T2G-Former achieves superior performance among DNNs and is competitive with non-deep Gradient Boosted Decision Tree models.
翻译:最近开发的用于表格学习的深层神经网络(DNN)在很大程度上得益于DNN的自动特征互动能力,然而,表格特征的异质性使这些特征相对独立,开发促进表格特征互动的有效方法仍然是一个尚未解决的问题。在本文件中,我们提议了一个新的图表模拟器,它自动估计表格特征之间的关系,并通过分配相关特征之间的边缘来建立图表。这种关系图将独立的表格特征组织成一种图表数据,以便节点(表层特征)的相互作用能够有序地进行。根据我们提议的图表模拟器,我们展示了专门为表格学习而设计的单调变换器网络,称为T2G-Former,它通过在关系图的指导下进行表态特征互动处理表格数据。具体的跨层次读取了T2G-Former各层次所预测的显著特征,并获得了最后预测的全球语义学。全面实验显示,我们的T2G-Former模型在DNNS中取得了优异性性,与不深的BOID决定具有竞争力。