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.

翻译：特征生成旨在生成新颖且有意义的特征，以构建具有判别性的表示空间。当生成的特征源于具备内在特征交互的特征对时，该特征即具有意义。在现实场景中，经验丰富的数据科学家能够从指数级庞大的搜索空间中，通过最优生成路径上的最优交叉形式，识别出潜在有用的特征交互关系，并生成有意义的维度。然而，机器缺乏类人能力。我们将此类学习任务泛化为自优化特征生成。自优化特征生成对现有系统提出了若干尚未充分解决的挑战：有意义的生成、鲁棒性生成以及高效生成。为应对这些挑战，我们提出了一种基于原则且通用的表示-交叉框架，以解决自优化特征生成问题。为实现哈希表示，我们提出了一种三步法：特征离散化、特征哈希与描述性摘要。为实现强化交叉，我们开发了一种层次化强化特征交叉方法。我们通过大量实验结果证明了所提方法的有效性与高效性。代码已开源在 https://github.com/yingwangyang/HRC_feature_cross.git。