论文标题
神经规则合奏：编码稀疏特征相互作用到神经网络
Neural Rule Ensembles: Encoding Sparse Feature Interactions into Neural Networks
论文作者
论文摘要
人工神经网络构成了非常强大的学习方法的基础。已经观察到，完全连接的神经网络的幼稚应用与许多无关变量的数据通常会导致过度拟合。为了避免此问题，与哪些功能相关的知识，并且可以将其可能的特征交互作用编码为这些网络。在这项工作中，我们使用决策树来捕获此类相关特征及其相互作用，并定义映射以编码提取的关系中的神经网络。这解决了完全连接的神经网络的初始化问题。同时，通过特征选择，与基于树木的方法相比，它可以学习紧凑的表示形式。经验评估和仿真研究表明，这种方法优于完全连接的神经网络和基于树的方法
Artificial Neural Networks form the basis of very powerful learning methods. It has been observed that a naive application of fully connected neural networks to data with many irrelevant variables often leads to overfitting. In an attempt to circumvent this issue, a prior knowledge pertaining to what features are relevant and their possible feature interactions can be encoded into these networks. In this work, we use decision trees to capture such relevant features and their interactions and define a mapping to encode extracted relationships into a neural network. This addresses the initialization related concern of fully connected neural networks. At the same time through feature selection it enables learning of compact representations compared to state of the art tree-based approaches. Empirical evaluations and simulation studies show the superiority of such an approach over fully connected neural networks and tree-based approaches
