A Pipeline Approach in Identifying Important Input Features from Neural Networks

Abstract

Neural networks are well-known for their powerful capability in producing high prediction accuracy. However, due to the non-linear calculations in the network, it is very difficult for users to understand which input features are important in leading to final predictions. In this study, we propose a two-step pipeline approach that uses two sets of linear models to estimates feature importance in the input dataset $X$ that leads to the class prediction specified in Y. More specifically, the first linear regression model derives the feature importance in $X$ in explaining the Z-code that was extracted from any hidden layer of a trained neural network. The second linear classification model captures the importance in the Z- code in predicting the target class Y. We then combine the first $X$ to $Z$ importance with the second $Z$ to $Y$ importance together to approximate the non-linear importance from $X$ to Y. The experiments conducted in this study also show that our method is sound and stable in selecting the truly important features from input datasets regardless how a neural network was constructed with different parameters such as activation functions or the number of hidden layers.