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Recent development of deep neural networks (DNNs) for tabular learning haslargely benefited from the capability of DNNs for automatic featureinteraction. However, the heterogeneity nature of tabular features makes suchfeatures relatively independent, and developing effective methods to promotetabular feature interaction still remains an open problem. In this paper, wepropose a novel Graph Estimator, which automatically estimates the relationsamong tabular features and builds graphs by assigning edges between relatedfeatures. Such relation graphs organize independent tabular features into akind of graph data such that interaction of nodes (tabular features) can beconducted in an orderly fashion. Based on our proposed Graph Estimator, wepresent a bespoke Transformer network tailored for tabular learning, calledT2G-Former, which processes tabular data by performing tabular featureinteraction guided by the relation graphs. A specific Cross-level Readoutcollects salient features predicted by the layers in T2G-Former acrossdifferent levels, and attains global semantics for final prediction.Comprehensive experiments show that our T2G-Former achieves superiorperformance among DNNs and is competitive with non-deep Gradient BoostedDecision Tree models.

T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature Interaction