  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.
