Feature crossing captures interactions among categorical features and is
useful to enhance learning from tabular data in real-world businesses. In this
paper, we present AutoCross, an automatic feature crossing tool provided by
4Paradigm to its customers, ranging from banks, hospitals, to Internet
corporations. By performing beam search in a tree-structured space, AutoCross
enables efficient generation of high-order cross features, which is not yet
visited by existing works. Additionally, we propose successive mini-batch
gradient descent and multi-granularity discretization to further improve
efficiency and effectiveness, while ensuring simplicity so that no machine
learning expertise or tedious hyper-parameter tuning is required. Furthermore,
the algorithms are designed to reduce the computational, transmitting, and
storage costs involved in distributed computing. Experimental results on both
benchmark and real-world business datasets demonstrate the effectiveness and
efficiency of AutoCross. It is shown that AutoCross can significantly enhance
the performance of both linear and deep models.

本文介绍了 4Paradigm 开发的自动特征交叉工具 AutoCross，通过树形空间的束搜索，实现高阶交叉特征的高效生成。同时，提出了连续的小批量梯度下降和多粒度离散化等算法来进一步提高效率和有效性，旨在减少分布式计算中涉及的计算、传输和存储成本。实验结果表明 AutoCross 可以显着提高线性和深度模型的性能。