A challenging open question in deep learning is how to handle tabular data. Unlike domains such as image and natural language processing, where deep architectures prevail, there is still no widely accepted neural architecture that dominates tabular data. As a step toward bridging this gap, we present DNF-Net a novel generic architecture whose inductive bias elicits models whose structure corresponds to logical Boolean formulas in disjunctive normal form (DNF) over affine soft-threshold decision terms. In addition, DNF-Net promotes localized decisions that are taken over small subsets of the features. We present an extensive empirical study showing that DNF-Nets significantly and consistently outperform FCNs over tabular data. With relatively few hyperparameters, DNF-Nets open the door to practical end-to-end handling of tabular data using neural networks. We present ablation studies, which justify the design choices of DNF-Net including the three inductive bias elements, namely, Boolean formulation, locality, and feature selection.

本文介绍了一种新颖的通用神经网络结构 DN F-Net，其归纳偏差促使其结构对应于包含于仿射软阈值决策项上的逻辑布尔公式的析取范式（DNF）。我们的实验证明，相对于全连接结构，在表格数据上，DNF-Net取得了显著而一致的性能提升，证明了其在神经网络中实现端到端的表格数据处理的潜在用处。

DNF-Net: 一种用于表格数据的神经结构