  Automated Feature Engineering (AFE) refers to automatically generate and
select optimal feature sets for downstream tasks, which has achieved great
success in real-world applications. Current AFE methods mainly focus on
improving the effectiveness of the produced features, but ignoring the
low-efficiency issue for large-scale deployment. Therefore, in this work, we
propose a generic framework to improve the efficiency of AFE. Specifically, we
construct the AFE pipeline based on reinforcement learning setting, where each
feature is assigned an agent to perform feature transformation \com{and}
selection, and the evaluation score of the produced features in downstream
tasks serve as the reward to update the policy. We improve the efficiency of
AFE in two perspectives. On the one hand, we develop a Feature Pre-Evaluation
(FPE) Model to reduce the sample size and feature size that are two main
factors on undermining the efficiency of feature evaluation. On the other hand,
we devise a two-stage policy training strategy by running FPE on the
pre-evaluation task as the initialization of the policy to avoid training
policy from scratch. We conduct comprehensive experiments on 36 datasets in
terms of both classification and regression tasks. The results show $2.9\%$
higher performance in average and 2x higher computational efficiency comparing
to state-of-the-art AFE methods.
