论文标题
通过特征交互检测和稀疏选择，稀疏交互添加网络
Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection
论文作者
论文摘要
当前，在统计严格的方法（如线性回归或添加激素）和使用神经网络的强大深度方法等统计严格的方法之间存在很大的性能。以前试图缩小此差距的工作未能完全研究成倍增长的功能组合数量，这些功能组合在训练过程中会自动考虑这些组合。在这项工作中，我们开发了一种可拖动的选择算法，以通过利用特征相互作用检测中的技术来有效地识别必要的特征组合。我们提出的稀疏相互作用添加剂网络（SIAN）构建了从这些简单且可解释的模型到完全连接的神经网络的桥梁。 Sian针对多个大型表格数据集的最新方法实现了竞争性能，并始终发现神经网络的建模能力与更简单方法的普遍性之间的最佳权衡。
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.
