There is currently a large gap in performance between the statistically rigorous methods like linear regression or additive splines and the powerful deep methods using neural networks. Previous works attempting to close this gap have failed to fully investigate the exponentially growing number of feature combinations which deep networks consider automatically during training. In this work, we develop a tractable selection algorithm to efficiently identify the necessary feature combinations by leveraging techniques in feature interaction detection. Our proposed Sparse Interaction Additive Networks (SIAN) construct a bridge from these simple and interpretable models to fully connected neural networks. SIAN achieves competitive performance against state-of-the-art methods across multiple large-scale tabular datasets and consistently finds an optimal tradeoff between the modeling capacity of neural networks and the generalizability of simpler methods.

翻译：目前，线性回归或加性样条等统计严谨的方法与使用神经网络的强大深度方法之间在性能上存在巨大差距。先前试图缩小这一差距的工作未能充分研究深度网络在训练过程中自动考虑的指数级增长的特征组合。在本工作中，我们开发了一种可处理的选取算法，通过利用特征交互检测技术高效识别必要的特征组合。我们提出的稀疏交互加性网络（SIAN）构建了从这些简单且可解释的模型到全连接神经网络的桥梁。SIAN在多个大规模表格数据集上取得了与最先进方法相竞争的性能，并始终能在神经网络的建模能力与简单方法的泛化性之间找到最优权衡。