  Feature generation aims to generate new and meaningful features to create a
discriminative representation space.A generated feature is meaningful when the
generated feature is from a feature pair with inherent feature interaction. In
the real world, experienced data scientists can identify potentially useful
feature-feature interactions, and generate meaningful dimensions from an
exponentially large search space, in an optimal crossing form over an optimal
generation path. But, machines have limited human-like abilities.We generalize
such learning tasks as self-optimizing feature generation. Self-optimizing
feature generation imposes several under-addressed challenges on existing
systems: meaningful, robust, and efficient generation. To tackle these
challenges, we propose a principled and generic representation-crossing
framework to solve self-optimizing feature generation.To achieve hashing
representation, we propose a three-step approach: feature discretization,
feature hashing, and descriptive summarization. To achieve reinforcement
crossing, we develop a hierarchical reinforcement feature crossing approach.We
present extensive experimental results to demonstrate the effectiveness and
efficiency of the proposed method. The code is available at
https://github.com/yingwangyang/HRC_feature_cross.git.
