Automatic Feature Engineering by Deep Reinforcement Learning

We present a framework called Learning Automatic Feature Engineering Machine (LAFEM), which formalizes the Feature Engineering (FE) problem as an optimization problem over a Heterogeneous Transformation Graph (HTG). We propose a Deep Q-learning on HTG to support efficient learning of fine-grained and generalized FE policies that can transfer knowledge of engineering "good" features from a collection of datasets to other unseen datasets.