Visible-hidden hybrid automatic feature engineering via multi-agent reinforcement learning

Feature engineering is one of the most important and time-consuming steps of machine learning algorithms. In recent years, automatic feature engineering (AFE) methods have received a lot of attention due to their low cost and scalability. However, the existing AFE methods do not take into account the interactions of features in the evolution process and cannot achieve effective feature selection, which weakens the performance of these methods. To tackle the above issues, a novel visible-hidden hybrid automatic feature engineering (VHAFE) method is proposed in this work. Specifically, a visible-hidden hybrid feature transformation graph (VHFTG) is devised to represent both various kinds of feature transformation functions and the feature selection process. Afterward, a multi-pointer state identification method is proposed, which enables the interactions among derived features and the efficient utilization of historically derived features. Furthermore, the multi-agent reinforcement learning algorithm is introduced to optimize the evolution process of VHFTG, where an input variable selection network is devised as the auxiliary policy network to avoid the generation of excessive noise features. Additionally, the VHAFE achieves the state-of-the-art results on a total of 19 public datasets and 1 dataset collected from the actual industrial operation process of gas turbines, which demonstrates the effectiveness of the proposed method.