AFGSL: Automatic Feature Generation based on Graph Structure Learning

Abstract

Feature engineering relies on domain knowledge and human intervention. To automate the process of feature engineering, automated feature construction methods use deep neural networks to capture feature interactions and attention coefficients to quantify the relationship between features. However, these methods ignore the influence of insignificant features that introduce noise and degrade the performance of the model. In this paper, we study the problem of feature interactions from the perspective of graph and propose a novel Automatic Feature Generation model based on Graph Structure Learning, called AFGSL. In this model, the adjacency matrix reflects the relationships between features, so that it can be used to filter out insignificant features. Furthermore, Q-learning is used to train the policy of stacking interaction layers, which enables it to make full use of both local and global information in the process of feature generation. The results of experiments on four real-world datasets show that AFGSL outperforms the state-of-the-art methods.