Venue: arXiv
Year: 2023
Paper: https://arxiv.org/abs/2301.02819
Abstract
Though neural networks have achieved enormous breakthroughs on various fields (e.g., computer vision) in supervised learning, they still trailed the performances of GBDTs on tabular data thus far. Delving into this issue, we identify that a proper handling of feature interactions and feature embedding is crucial to the success of neural networks on tabular data. We develop a novel neural network called ExcelFormer, which alternates in turn two attention modules that respectively manipulate careful feature interactions and feature embedding updates. A bespoke training methodology is jointly introduced to facilitate the model performances. By initializing parameters with minuscule values, these attention modules are attenuated when the training begins, and the effects of feature interactions and embedding updates progressively grow up to optimum levels under the guidance of the proposed specific regularization approaches Swap-Mix and Hidden-Mix as the training proceeds. Experiments on 25 public tabular datasets show that our ExcelFormer is superior to extremely-tuned GBDTs, which is an unprecedented achievement of neural networks in supervised tabular learning.