  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.
